Categorizing the Content of GitHub README Files

02/20/2018 ∙ by Gede Artha Azriadi Prana, et al. ∙ Singapore Management University The University of Adelaide 0

README files play an essential role in shaping a developer's first impression of a software repository and in documenting the software project that the repository hosts. Yet, we lack a systematic understanding of the content of a typical README file as well as tools that can process these files automatically. To close this gap, we conduct a qualitative study involving the manual annotation of 4,226 README file sections from 393 randomly sampled GitHub repositories and we design and evaluate a classifier and a set of features that can categorize these sections automatically. We find that information discussing the `What' and `How' of a repository is very common, while many README files lack information regarding the purpose and status of a repository. Our multi-label classifier which can predict eight different categories achieves an F1 score of 0.746. This work enables the owners of software repositories to improve the quality of their documentation and it has the potential to make it easier for the software development community to discover relevant information in GitHub README files.



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1 Introduction and Motivation

The file for a repository on GitHub is often the first project document that a developer sees when they encounter a new project. This first impression is crucial, as Fogel Fogel (2005) states: “The very first thing a visitor learns about a project is what its home page looks like. […] This is the first piece of information your project puts out, and the impression it creates will carry over to the rest of the project by association.”

With more than 25 million active repositories at the end of 2017,111 GitHub is the most popular version control repository and Internet hosting service for software projects. When setting up a new repository, GitHub prompts its users to initialize the repository with a file which by default only contains the name of the repository and is displayed prominently on the homepage of the repository.

A recent blog post by Christiano Betta222 compares the README files of four popular GitHub repositories and stipulates that these files should (1) inform developers about the project, (2) tell developers how to get started, (3) document common scenarios, and (4) provide links to further documentation and support channels. In its official documentation,333 GitHub recommends that a README file should specify “what the project does, why the project is useful, how users can get started with the project, where users can get help with your project, and who maintains and contributes to the project”. Brian Doll of GitHub claimed in a recent interview for IEEE Software that “the projects with good README files tend to be the most used, too, which encourages good README writing behavior” Begel et al (2013).

In the research literature, GitHub README files have been used as a source for automatically extracting software build commands Hassan and Wang (2017), developer skills Greene and Fischer (2016); Hauff and Gousios (2015), and requirements Portugal and do Prado Leite (2016). Their content has also played a role in cataloguing and finding similar repositories Sharma et al (2017); Zhang et al (2017) as well as in analyzing package dependency problems Decan et al (2016).

However, up to now and apart from some anecdotal data, little is known about the content of these README files. To address this gap, our first research questions RQ1 asks, What is the content of GitHub README files? Knowing the answer to this question would still require readers to read an entire file to understand whether it contains the information they are looking for. Therefore, our second research question RQ2 investigates, How accurately can we automatically classify the content of sections in GitHub README files?. We supplement this investigation with an analysis of a README file’s most defining features in answer to RQ3, What value do different features add to the classifier?

To answer our research questions, we report on a qualitative study of a statistically representative sample of 393 GitHub README files containing a total of 4,226 sections. Our annotators and ourselves annotated each section with one or more codes from a coding schema that emerged during our initial analysis. This annotation provides the first large-scale empirical data on the content of GitHub README files. We find that information discussing the ‘What’ and ‘How’ of a repository is common while information on purpose and status is rare. These findings provide a point of reference for the content of README files that repository owners can use to meet the expectations of their readers as well as to better differentiate their work from others.

In addition to the annotation, we design a classifier and a set of features to predict categories of sections in the README files. We believe that this will enable both quick labeling of the sections and subsequent discovery of relevant information. We evaluated the classifier’s performance on the manually-annotated dataset, and identify the most useful features for distinguishing the different categories of sections. Our evaluation shows that the classifier achieves an F1 score of 0.746. Also, the most useful features are commonly related to some particular words, either due to their frequency or their unique appearance in sections’ headings.

We make the following contributions:

  • A qualitative study involving the manual annotation of the content of 4,226 sections from 393 randomly selected GitHub README files, establishing a point of reference for the content of a GitHub README file. We distinguish eight categories in the coding schema that emerged from our qualitative analysis (What, Why, How, When, Who, References, Contribution, and Other), and we report their respective frequencies and associations.

  • We design and evaluate a classifier that categorizes README sections, based on the categories discovered in the annotation process.

We describe our research methodology in Section 3 and the results of our manual annotation in Section 4. Section 5 introduces the classifier we built for sections of GitHub README files, which we evaluate in Section 6. We discuss the implications of our work in Section 7 and present the threats to validity associated with this work in Section 8. We review related work in Section 9 before we conclude in Section 10.

2 Background

GitHub is a code hosting platform for version control and collaboration.444 Project artifacts on GitHub are hosted in repositories which can have many branches and are contributed to via commits. Issues and pull requests are the primary artifacts through which development work is managed and reviewed.

Due to GitHub’s pricing model which regulates that public projects are always free,555 GitHub has become the largest open source community in the world, hosting projects from hobby developers as well as organizations such as Adobe, Twitter, and Microsoft.666

Each repository on GitHub can have a README file to “tell other people why your project is useful, what they can do with your project, and how they can use it.”777 README files on GitHub are written in GitHub Flavored Markdown, which offers special formatting for headers, emphasis, lists, images, links, and source code, among others.888 Figure 1 shows the README file of D3, a JavaScript library for visualizing data using web standards.999 The example shows how headers, pictures, links, and code snippets in markdown files are represented by GitHub.

Figure 1: An excerpt from D3’s GitHub README file

With 1 billion commits, 12.5 million active issues, and 47 million pull requests in the last 12 months, GitHub plays a major role in today’s software development landscape.101010 In 2017, 25 million active repositories were competing for developers’ attention, and README files are among the first documents that a developer sees when encountering a new repository. In this work, we study and classify the content of these README files.

3 Research Methodology

In this section, we present our research questions and describe the methods for data collection and analysis.

3.1 Research Questions

Our work was guided by three research questions, which focus on categorizing the content of GitHub README files and on evaluating the performance of our classifier:


What is the content of GitHub README files?

Answers to this question will give insight to repository maintainers and users about what a typical README file looks like. This can serve as a guideline for repository owners who are trying to meet the expectations of their users, and it can also point to areas where owners can make their repositories stand out among other repositories.


How accurately can we automatically classify the content of sections in GitHub README files?

Even after knowing what content is typically present in a GitHub README file, readers would still have to read an entire file to understand whether it contains the kind of information they are looking for. An accurate classifier that can automatically classify sections of GitHub README files would render this tedious and time-consuming step unnecessary. From a user perspective, an automated classifier would enable a more structured approach to searching and navigating GitHub README files.


What value do different features add to the classifier?

Findings to our last research question will help practitioners and researchers understand the content of README files in more detail and shed light on their defining features. These findings can also be used in future work to further improve the classification.

3.2 Data Collection

To answer our research questions, we downloaded a sample of GitHub files111111We only consider files in our work since these are the ones that GitHub initializes automatically. GitHub also supports further formats such as README.rst, but these are much less common and out of scope for this study. by randomly selecting GitHub repositories until we had obtained a statistically representative sample of files that met our selection criteria. We excluded README files that contained very little content and README files from repositories that were not used for software development. We describe the details of this process in the following paragraphs.

To facilitate the random selection, we wrote a script that retrieves a random GitHub repository through the GitHub API using the API call, where number is the repository ID and was replaced with a random number between 0 and 100,000,000, which was a large enough number to capture all possible repositories at the time of our data collection. We repeated this process until we had retrieved a sufficient number of repositories so that our final sample after filtering would be statistically representative. We excluded repositories that did not contain a README file in the default location.

Reason for Exclusion Repositories
Software, but small README file, i.e., 2 KB 429
Not software, but large enough README file 127
Not software and small README file 196
README file not in English 48
Number of repositories included in the sample 393
Total number of repositories inspected 1,193
Table 1: Number of repositories excluded from the sample

Following the advice of Kalliamvakou et al. Kalliamvakou et al (2014), we further excluded repositories that were not used for software development by inspecting the programming languages automatically detected for each repository by GitHub. If no programming language was detected for a repository, we excluded this repository from our sample. We also excluded repositories for which the README file was very small. We considered a file to be very small if it contained less than two kilobytes of data. This threshold was set based on manual inspection of the files which revealed that files with less than two kilobytes of content typically only contained the repository name, which is the default content of a new README file on GitHub.

During the manual annotation (see Section 2.4), we further excluded README files if their primary language was not English. Table 1

shows the number of repositories excluded based on these filters. Our final sample contains 393 README files, which results in a confidence interval of 4.94 at a confidence level of 95% for our conclusions regarding the population of all GitHub repositories, assuming a population of 20 million repositories.





Figure 2: Number of sections per README file in our sample

We then used GitHub’s markdown121212 to extract all sections from the README files in our sample, yielding a total of 4,226 sections distributed over the 393 README files. GitHub’s markdown offers headers at different levels (equivalent to HTML’s h1 to h6 tags) for repository owners to structure their README files. Figure 2 shows the distribution of the number of sections per README file. The median value is seven and 50% of the files contain between five and twelve sections.

3.3 Coding schema

We adopted ‘open coding’ since it is a commonly used methodology to identify, describe, or categorise phenomena found in qualitative data Corbin and Strauss (1990). In order to develop a coding scheme, one author manually classified a random sample of fifty README contents into meaningful categories (known as codes Miles and Huberman (1994)). Our findings from this examination consist of a tentative list of seven categories (e.g. what, why, how) and sub categories (e.g., introduction, background). After defining initial codes, we trialed them on 150 README sections using two annotators. For this round of coding, we obtained inter-rater reliability of 76%. Following this trial, we refined our codes until we reach agreement on a scheme that contained codes for all of the types of README sections we encountered. Finally, we define the ‘other’ category only when all other possibilities have been exhausted. Table 2 shows the finalised set of categories and sub categories. The categories roughly correspond to the content of README files that is recommended by GitHub (cf. Introduction). We identified the first category (‘What’) based on headings such as ‘Introduction’ and ‘About’, or based on the text at the beginning of many README files. We found that either a brief introduction or a detailed introduction is common in our dataset. Conversely, category two (‘Why’) is rare in README files. For instance, some repositories compare their work to other repositories based on factors such as simplicity, flexibility, and performance. Others list advantages of their project in the introduction.

The most frequent category is ‘How’ since the majority of README files tend to include instructions on how to use the project such as programming-related content (e.g., configuration, installation, dependencies, and errors/bugs). Table 2 lists a sample of section headings that belongs to the ‘How’ category. Further, it is also important to the reader of a README file to be familiar with the status of the project, including versions as well as complete and in-progress functionality. We categorize this kind of time-related information into the fourth code (‘When’).

We categorize sections as ‘Who’ content when they include information about who the project gives credit to. This could be the project team or acknowledgements of other projects that are being reused. This category also includes information about licence, contact details, and code of conduct. The second most frequent category is ‘References’. This category includes links to further details such as API documentation, getting support, and translations. This category also includes ‘related projects’, which is different from the ‘comparison with related projects’ in category ‘Why’ due to the lack of an explicit comparison. Our final category is ‘Contribution’, which includes information about how to fork or clone the repository, as well as details on how to contribute to the project. Our manual analysis indicated that some repositories include separate files which contain instructions on how to get involved with the project. We do not consider files in this study. In addition, we included a category called ‘Other’ which is used for sections that do not belong to any of the aforementioned seven categories.

# Category Sub-category
1 What Introduction, project background
2 Why
Advantages of the project,
comparison with related work
3 How
Getting started/quick start, how to run,
installation, how to update, configuration,
setup, requirements, dependencies,
languages, platforms, demo,
downloads, errors and bugs
4 When
Project status, versions,
project plans, roadmap
5 Who
Project team, community,
mailing list, contact, acknowledgement,
licence, code of conduct
6 References
API documentation, getting support,
feedback, more information,
translations, related projects
7 Contribution Contributing guidelines
8 Other
Table 2: README section coding reference

3.4 Manual annotation

We initially used two annotators to code the dataset. One of the annotators was a PhD candidate specializing in Software Engineering while the other one is an experienced Software Engineer working in the industry. Neither of the annotators is an author of this paper. Each annotator spent approximately thirty hours to annotate the dataset. The task of an annotator is to read the section headings and contents and assign a code based on the coding reference. The annotators assign codes from the eight available codes (Table 2). Each section of a README file can have one or more codes.

We measured the inter-rater agreement between the two annotators and obtained a percentage agreement of 66% (i.e., the codes of both annotators are identical). We also had another 10.7% of partial agreement. This occurs when annotators mention more than one code and only a fraction of the codes overlap. The remaining 23.3% of the annotations had no agreement. We used a third annotator to rectify the sections which had no agreement. For this, two authors of the paper (Software Engineering academics) co-annotated the remaining sections that had no agreement. For all cases, we then used a majority vote to determine the final set of codes for each section, i.e., all codes that had been used by at least two annotators for a section were added to the final set of codes for that section.131313In cases where there was perfect agreement between the two annotators, the majority vote rule simply yields the codes that both annotators agreed on. In very few cases, there was still no agreement on any set of codes after considering the codes from three annotators. These cases were manually resolved by discussion between two authors of this paper.

We manually examined the instances where the annotators disagree. Annotators were likely confused when the README includes ‘Table Of Contents (TOC)’ as they have provided inconsistent codes in these instances. Since TOC is included at the beginning of the file, one annotator considers it as category ‘What’ while the other one placed it in the references. However, the third annotator categorized TOC into ‘Other’, which is what we used in the final version of the annotated dataset. Another common confusion occurred when categorizing ‘community-related’ content. Our coding reference (Table 2) suggests that community-related information should be placed in the ‘Who’ category. However, one annotator identified it in the ‘Contribution’ category. We generally resolved ‘community-related’ disagreements by placing them into the ‘Who’ category, in accordance with our coding guide.

We also noticed that our annotators are reluctant to place content into the ‘Other’ category. Instead, they attempted to classify README contents into the other seven categories. Further, one of the main reasons for disagreement between the two annotators were instances where platforms and languages that support the project had external links. For example, one README file listed the middleware available to use with their project. However, their list includes URLs to apache and nginx. One annotator categorized these sections into ‘How’ while the other placed them in additional resources (code ‘References’) since they have external links. There can be multiple headings which depend on this decision. For instance, one README file contained 36 headings about configurations. They are categorized into ‘How’ by one annotator while the other one placed them in additional resources since they have URLs. Resolving this disagreement affected many sections at once.

Further, some README files include screenshots or diagrams to provide an overview or demonstrations. These are expected to be classified in ‘Other’. However, annotators have occasionally assigned codes such as ‘What’, ‘How’, and ‘References’ to image contents. Another challenging decision is when repositories include all the content under a single heading. This causes the annotators to assign multiple codes which possibly do not overlap between annotators. In addition, we sometimes found misleading headings such as ‘how to contribute’ where the heading would suggest that the content belongs to category ‘Contribution’. However, in a few cases, the content of this section included information on ‘how to use the project’ (i.e., download, install, and build).

4 The content of GitHub README files

Table 3 demonstrates the distribution of categories based on the human annotation (columns 3 & 4 on ‘sections’) and the README files in our sample (last two columns on ‘files’). Based on manually annotated sections, the most frequent category is ‘How’ (58.4%), while the least frequent was ‘Other’ (1.6%). As mentioned previously, as part of the coding, our annotators also excluded non-English content that had not been detected by our automated filters (code ‘-’). The same applies to parts of README files that had been incorrectly detected as sections by our automated tooling.

# Category # Sections Sections % # Files Files %
1 What 708 16.8% 381 97.0%
2 Why 118 2.8% 101 25.7%
3 How 2,470 58.4% 348 88.5%
4 When 184 4.4% 84 21.4%
5 Who 327 7.7% 208 52.9%
6 References 864 20.4% 239 60.8%
7 Contribution 129 3.1% 109 27.8%
8 Other 66 1.6% 27 6.9%
- Exclusion 696
Table 3: Distribution of README categories
# Codes # Sections
5 2
4 6
3 40
2 498
1 3,680
Total 4,226
Table 4: Quantity of codes per section

Based on the consideration of files in our sample (last two columns of Table 3), 97% of the files contain at least one section describing the ‘What’ of the repository and 88.5% offer some ‘How’ content. Other categories, such as ‘Contribution’, ‘Why’, and ‘Who’, are much less common.

Further, we report the distribution of number of codes across the sections of GitHub README files in our sample (Table 4). The sections that are annotated using four or five codes mostly stem from README files that only contain a single section. Interestingly, the majority of these files include ‘What’, ‘Who’, and ‘References’. Also, 92% of the sections which are annotated using three codes include ‘What’. Unsurprisingly, the most popular combination of two codes was ‘How’ and ‘References’, enabling access to additional information when learning ‘how to use the project’. These relationships are further explored in the following section.

4.1 Relations between codes

To understand relations between the different categories of README content, we applied association rule learning Agrawal et al (1993) to our data using the arules package in R. To find interesting rules, we grouped the data both by sections (i.e., each section is a transaction) and by files (i.e., each file is a transaction).

Rule Support Confidence
{Why, How} {What} 0.002 1.00
{Why, References} {What} 0.003 0.93
Table 5: Association rules at section level
Rule Support Confidence
{Who} {What} 0.52 0.98
{How, References} {What} 0.54 0.98
{References} {What} 0.59 0.97
{How} {What} 0.86 0.97
{References} {How} 0.55 0.91
{What, References} {How} 0.54 0.91
{What} {How} 0.86 0.89
Table 6: Association rules at file level

Table 5 shows the extracted rules at section level. We only consider rules with a support of at least 0.0013 (i.e., the rule must apply to at least five sections) and a confidence of at least 0.8. Due to the small number of sections for which we assigned more than one code, only two rules were extracted: Sections that discuss the ‘Why’ and ‘How’ are likely to also contain information on the ‘What’. Similarly, sections that discuss the ‘Why’ of a project and contain ‘References’ are also likely to contain information on the ‘What’.

At file level, we were able to find more rules, see Table 6. For these rules, we used a minimum support of 0.5 and a minimum confidence of 0.8. We chose a minimum support of 0.5 to limit the number of rules to the most prevalent ones which are supported at least by half of the README files in our dataset. The rules extracted with these parameters all imply ‘What’ or ‘How’ content to be present in a README file. For example, we have a 98% confidence that a file that contains information about ‘Who’ also contains information about the ‘What’ of a project. This rule is supported by 52% of the README files in our dataset.

4.2 Examples

In this section, we present an example for each of the categories to illustrate the different codes.


The leading section of the GitHub README file of the ParallelGit repository141414 by GitHub user jmilleralpine is a simple example of a section that we would categorize into the ‘What’ category. The section header simply restates the project name (“ParallelGit”) and is followed by this brief description: “A high performance Java JDK 7 nio in-memory filesystem for Git.” Since this is an introduction to the project, we assign the code ‘What’.


The README file of the same repository (ParallelGit) also contains a section with the heading “Project purpose explained” which we categorize into the ‘Why’ category. This section starts with a list of four bullet points outlining useful features of Git, followed by a brief discussion of the “lack of high level API to efficiently communicate with a Git repository”. The README file then goes on to explain that “ParallelGit is a layer between application logic and Git. It abstracts away Git’s low level object manipulation details and provides a friendly interface which extends the Java 7 NIO filesystem API.” Since this section describes the purpose of the project and motivates the need for it, we assign the code ‘Why’.


The same README file also contains a section with the heading “Basic usages”, which we classify into the ‘How’ category. It provides two short code snippets of seven and eight lines, respectively, which illustrate the use cases of “Copy a file from repository to hard drive” and “Copy a file to repository and commit”. We assign the code ‘How’ because this section explains how to run the software.


An example of a section discussing the ‘When’ aspect of a project is given by the section with the heading “Caveats” of the Sandstorm repository151515 by GitHub user solomance. The project is a self-hostable web app platform. In its “Caveats” section, the README file states “Sandstorm is in early beta. Lots of features are not done yet, and more review needs to be done before relying on it for mission-critical tasks. That said, we use it ourselves to get work done every day, and we hope you’ll find it useful as well.” Since this section describes the project status, we assign the code ‘When’.


Going back to the README file of the ParallelGit repository, it concludes with a section with the heading “License” and the following text: “This project is licensed under Apache License, Version 2.0.” A link to the license text is also included. We categorized this section under ‘Who’ since it contains licence information (see Table 2).


The previously mentioned README file of the Sandstorm repository also contains sections that we categorized as ‘References’, e.g., the section with the heading “Using Sandstorm”. This section only contains the statement “See the overview in the Sandstorm documentation” which links to more comprehensive documentation hosted on We assign the code ‘References’ since the section does not contain any useful content apart from the link to more information. This section showcases one of the challenges of classifying the content of sections contained in GitHub README files: While the section header suggests that the section contains ‘How’ information, the body of the section reveals that it simply contains a link.


The README file of Sandstorm also contains a section with the heading “Contribute” which we categorized under ‘Contribution’. The section states “Want to help? See our community page or get on our discussion group and let us know!” and contains links to a community page hosted on as well as a discussion group hosted on Google Groups.161616 We assign the code ‘Contribution’ rather than ‘References’ since this section contains information other than links, i.e., the different ways in which contributions can be made. Arguably, this is a corner case in which the code ‘References’ would also be justifiable.


An example of a section that we were not able to categorize using any of the previous seven categories is the last section in the README file of the Blackjack repository171717 by GitHub user ChadLactaoen. The section does not contain any content and simply consists of the section heading “Have fun!” In this case, the section feature of GitHub markdown was used for highlighting rather than for structuring the content of the README file. We therefore categorized the section as ‘Other’.

5 A GitHub README Content Classifier

In this section, we describe our automated classification approach for classifying GitHub README content. We first describe the overall framework of our approach and then explain each of its steps. For the development of this classifier, we split the dataset into two, a development set comprising 25% of the data, and an evaluation set comprising 75% of the data. We analyze and use the development

set to design features for the classifier, such as heuristics based on language patterns (see Section

5.2.2). The evaluation set is the hold out set that is used for evaluation of the classifier through ten-fold cross-validation. A similar process of dividing a dataset into two – one for manual analysis for feature identification, and another for evaluation – has been done in prior studies (e.g., Panichella et al (2015)) to improve reliability of reported results.

5.1 Overall Framework

Figure 3: The overall framework of our automated GitHub README content classifier.

We present the overall framework of our automated classification approach in Figure 3. The framework consists of the following steps:

  1. Feature Extraction: From each section of the annotated GitHub README files, we extract meaningful features that can identify categories of a section’s content. We extract statistical and heuristic features. These features are output to the next step for learning.

  2. Classifier Learning: Using features from the previous step, we learn a classifier that can identify the categories that the content of each section belongs to. Since each section can belong to many categories, we use a multi-label classifier, which can output several categories for each section.

  3. Validation: To choose our classifier setting, we need to validate our classifier performance on a hold out set. We experiment with different settings and pick the classifier that performs the best on the hold out set.

We explain details of the above steps in the next subsections.

5.2 Feature Extraction

From the content of each section, we extract two sets of features: statistical features and heuristic features.

5.2.1 Statistical Features

These features compute word statistics of a README section. These features are constructed from combination of both heading and content of the section. To construct these features, the section’s content and heading are first preprocessed. We perform two preprocessings: content abstraction and tokenization. Content abstraction abstracts contents to their types. We abstract the following types of section content: mailto link, hyperlink, code block, image, and numbers. Each type is abstracted into a different string (@abstr_mailto, @abstr_hyperlink, @abstr_code_section, @abstr_image and @abstr_number, respectively). Such abstraction is performed since for classification, we are more interested in existence of those types in a section than its actual content. For example, existence of a source code block in a section may indicate that the section demonstrates usage of the project, regardless of the source code. With abstraction, all source code blocks are converted to the same string, and subsequently, into the same statistical feature. This abstraction is followed by tokenization, which converts a section into its constituent words, and English stop word removal. For the stop word removal, we use the stop words provided by scikit-learn Pedregosa et al (2011).

After preprocessing, we count the number of times a word appears in each section. This is called the Term Frequency (TF) of a word in a section. If there are words that appear in the set of sections used for training the classifier (after preprocessing), we would have statistical features for each section. If a word does not appear in a section, then its TF is zero. We also compute the Inverse Document Frequency (IDF) of a word. IDF of a word is defined as the reciprocal of the number of sections in which the word appears. We use a multiplication of TF and IDF as an information retrieval feature for a particular word.

5.2.2 Heuristic Features

There has been work such as Panichella et al. Panichella et al (2015) which exploits recurrent linguistic patterns within a category of sentences to derive heuristics that can aid classification. Given this, we manually inspected the content of various sections in the development set to try to identify patterns that may be useful to distinguish each category. The following are the resulting heuristic features that we use for the classifier.

  1. Linguistic Patterns: This is a binary feature that indicates whether a particular linguistic pattern exists in a section. We discover linguistic patterns by looking at words/phrases that either appear significantly more in one particular category or are relatively unique to a particular category. A linguistic pattern is tied to either a section’s heading or content. A pattern for heading is matched only to the section’s heading. Similarly, a pattern for content is matched only to the section’s content. There are 55 linguistic patterns that we identified.181818The linguistic patterns are available in

  2. Single-Word Non-English Heading: This is a binary feature that indicates whether a section’s heading is a single word non-English heading. An example is a method name, which may be used as heading in a section describing the method and usually belong to the ‘How’ category. This check is performed by checking the word against the wordlist corpora from NLTK Bird et al (2009).

  3. Repository Name: This is a binary feature that indicates whether any word in the repository name is used in a section’s heading. This is based on the observation that the README section that provides an overview of the project likely contains common words from the project name. For example, a repository of a project called ‘X’ will contain ‘X’ in its name, and the README section providing an overview of the project may be given a heading along the lines of ‘About X’, ‘Overview of X’, or ‘Why X’. This is different, for example, from README sections containing licence information or additional resources.

  4. Non-ASCII Content Text: This is a binary feature that indicates whether a section contains any non-ASCII character. It is based on the observation that README sections containing text written in non-ASCII characters tend to be categorized as ‘Exclusion’, although they often also contain parts (e.g., technical terms or numbers) written in ASCII characters.

5.3 Classifier Learning

Given the set of features from the previous step, we construct a multi-label classifier that can automatically categorize new README sections. We use a binary relevance method for multi-label classification Luaces et al (2012). This method transforms the problem of multi-label classification into a set of binary classifications. Due to the small number of entries in the ‘Why’ category, combined with the fact that a large proportion of content in this category is also assigned to the ‘What’ category, we combined the two categories. We therefore ended up with eight categories including ‘Exclusion’, and subsequently created eight binary classifiers, each for a particular category.

A binary classifier for a particular label considers an instance that contains the label as a positive instance, otherwise it is a negative instance. As such, the training set for the binary classifier is often imbalanced. Thus, we balance the training set by performing oversampling. In this oversampling, we duplicate instances of minority classes and make sure that each instance is duplicated roughly until we have the same number of positive and negative instances in the set.

5.4 Validation

In this step, we determine the classifier setting by performing ten-fold cross validation. In ten-fold cross validation, we divide the validation set into ten sets, each having a roughly equal number of instances. We then run the validation ten times. Each time we use a particular set for validating the classifier performance and the remaining sets for training the classifier. Classifier performances over the ten validations are then averaged. A setting that leads to the highest classifier performance is selected.

6 Evaluation of the Classifier

We conduct experiments with our classifier on the dataset annotated in Section 4. We evaluate the classifier on the evaluation set using ten-fold cross validation. We follow our framework in Section 5 to construct our classifier. For evaluation, the TF-IDF vocabulary is constructed from the evaluation set, and is not shared with the development set. The size of this vocabulary created from the evaluation

set is 14,248. We experiment with the following classification algorithms: Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), and

-Nearest Neighbors (kNN). We use implementations of the classification algorithms from

scikit-learn Pedregosa et al (2011).

Evaluation metric

We measure the classification performance in terms of F1 score. F1 score for multi-label classification is defined below.

where is the proportion of the actual label in all predicted data. is the F1 score for label , is the set of labels, is precision for label , and is the recall for label . When computing precision/recall for label , an instance having label is considered as a positive instance, otherwise it is a negative instance. Precision is the proportion of predicted positive instances that are actually positive while recall is the proportion of actual positive instances that are predicted as positive.

Evaluation results

The results of our evaluation are shown in Table 7. Our experimental results show that our classifier can achieve an F1 score of 0.746 on the evaluation set using ten-fold cross validation. We also show per category F1 in Table 8.

SVM 0.746
RF 0.696
NB 0.518
LR 0.739
kNN 0.588
Table 7: Results for Different Classifiers
What and Why 0.615
How 0.861
When 0.676
Who 0.758
References 0.605
Contribution 0.814
Other 0.303
Exclusion 0.674
F1 0.746
Table 8: Effectiveness of Our Classifier
Multi-category sections vs. single-category sections

We expect that classifying multi-category sections is harder than classifying single-category sections. To confirm this, we exclude sections that belong to more than one category. We perform a similar experiment using ten-fold cross validation. Our experimental results show that our classifier achieves an F1 score of 0.773, which confirms that classifying single-category sections is indeed easier.

Usefulness of statistical vs. heuristic features

To investigate the value of a set of features, we remove the set and observe the classifier performance after such removal. Table 9 shows the classifier performance when we remove different sets of features. We observe performance reduction when removing any set of features. Thus, all sets of features are valuable for classifying README sections. Among the sets of features, the statistical features are more important since their removal reduces F1 far more as compared to removing heuristic features.

Set of Features Used F1
Only Heuristic 0.584
Only Statistical 0.706
Table 9: Contribution of Different Sets of Features
Usefulness of particular features

We are also interested in identifying which particular feature is more useful when predicting different categories. Using an SVM classifier, usefulness of a feature can be estimated based on the weight that the classifier assigns to the feature. For each category in the testing data, we consider an instance belonging to the category as a positive instance, otherwise it is a negative instance. We learn an SVM classifier to get the weight of each feature. To capture significantly important features, we perform the Scott-Knott ESD (Effect Size Difference) test 

Tantithamthavorn et al (2017). For the purpose of this test, we perform ten times ten-fold cross validation where each cross validation generates different sets. Thus, for each category and feature pair, we have 100 weight samples. We average the weights and run Scott-Knott ESD test on the top-5 features’ weights. We present the result for each category in Figure 4. Features grouped by the same color are considered to have a negligible difference and thus have the same importance.

Based on the observation, heuristics based on sections’ headings appear to be useful in predicting categories. For example, heur_h_k_012 (check whether a lower cased heading contains the string ‘objective’) is the second most useful features for predicting the ‘What and Why’ category, while heur_h_k_006 (check whether a lower cased heading contains the string ‘contrib’) is the third most useful feature for predicting ‘Contribution’ category. For the ‘Who’ category, heur_h_k_007 (check whether a lower cased heading contains ‘credit’) is the fifth most useful feature for prediction. Abstraction also appears to be useful, with @abstr_number being the fifth ranking feature for predicting ‘When’ category. A possible reason is that the ‘When’ category covers version history, project plans, and project roadmap, which often contain version number, year, or other numbers.

Figure 4: Top Features for Each Category. Features starting with heur_ refer to heuristic features while the remaining features refer to statistical features (see Section 4.2).

7 Implications

The ultimate goal of our work is to enable the owners of software repositories on sites such as GitHub to improve the quality of their documentation, and to make it easier for the users of the software held in these repositories to find the information they need.

The eight categories of GitHub README file content that emerged from our qualitative analysis build a point of reference for the content of such README files. These categories can help repository owners understand what content is typically included in a README file, i.e., what readers of a README file will expect to find. In this way, the categories can serve as a guideline for a README file, both for developers who are starting a new project (or who are starting the documentation for an existing project) and developers who want to evaluate the quality of their README file. Even if all the content is in place, our coding reference provides a guide on how to organize a README file.

In addition, the categories along with their frequency information that we report in this paper highlight opportunities for repository owners to stand out among a large crowd of similar repositories. For example, we found that only about a quarter of the README files in our sample contain information on the ‘Why’ of a repository. Thus, including information on the purpose of a project is a way for repository owners to differentiate their work from that of others. It is interesting to note that out of all the kinds of content that GitHub recommends to include in a README file (cf. Introduction), ‘Why’ is the one that is the least represented in the README files of the repositories in our sample.

In a similar way, README content that refers to the ‘When’ of a project, i.e., the project’s current status, is rare in our sample. In order to instill confidence in its users that they are dealing with a mature software project and to possibly attract users to contribute to a project, this information is important. However, our qualitative analysis found that less than a quarter of the repositories in our random sample included ‘When’ information.

The ratio of repositories containing information about how to contribute was slightly higher (109/393), yet surprisingly low given that all of the repositories in our sample make their source code available to the public. Given recent research on the barriers experienced by developers interested in joining open source projects Steinmacher et al (2016), our findings provide another piece of evidence that software projects have room for improvement when it comes to making a good first impression Fogel (2005) and explaining how developers can contribute.

The classifier we have developed can automate the task of analyzing the content of a README file according to our coding reference, a task that would otherwise be tedious and time-consuming. Our classifier can take any GitHub README file as input and classify its content according to our codes with reasonable precision and recall.

In addition to automatically classifying the content, our classifier could enable semi-structured access to the often unstructured information contained in a GitHub README file. For example, users particularly interested in finding mature projects could automatically be brought to the ‘When’ sections of a README file, and developers looking to contribute to open source could be shown the ‘Contribution’ guidelines of a repository.

Our classifier could also easily be used to help organize README files, e.g., by imposing a certain order in which sections should appear in a README file. README sections that have been detected as discussing the ‘What’ and ‘Why’ of a project could automatically be moved to the beginning of a README file, followed by sections discussing the ‘How’.

Our analysis of the usefulness of features for predicting the categories of a section implies that heuristic features on the sections’ headings are useful, and are better suited than heuristic features on the sections’ contents. This is apparent from the fact that none of the heuristic features for sections’ contents are ranked among the top-5 most useful features for any of the categories. This suggests that the vocabulary commonly used in section headings is more uniform than that used in section content. However, we note that the 4,226 sections in our dataset use 3,080 distinct headings, i.e., only few of the sections share the same heading.

8 Threats to Validity

Similar to other empirical studies, there are several threats to the validity of our results.

Threats to the construct validity correspond to the appropriateness of the evaluation metrics. We use F1 as our evaluation metric. F1 has been used in many software engineering tasks that require classification 

Kim et al (2008); Rahman et al (2012); Nam et al (2013); Canfora et al (2013); Rahman and Devanbu (2013). Thus, we believe threats to construct validity are minimal.

Threats to the internal validity compromise our confidence in establishing a relationship between the independent and dependent variables. It is possible that we introduced bias during the manual annotation of sections from GitHub README files. We tried to mitigate this threat by using two annotators, and by manually resolving all cases in which the two annotators disagreed. We did however notice a small number of cases where annotators mistakenly treated non-sections (e.g., content that had been commented out) as sections.

Threats to external validity correspond to the ability to generalize our results. While our sample of 393 GitHub README files is statistically representative, it is plausible that a different sample of files would have generated different results. We can also not claim generalizability to any other format of software documentation. We excluded README files that were small (less than 2 KB in size), README files that belonged to repositories not used for software development, and README files not in English. Different filtering criteria might have led to different results. Our findings may also have been impacted by our decision to divide README files into sections. A different way of dividing README files (e.g., by paragraphs or sentences) might also have produced different results.

9 Related Work

Efforts related to our work can be divided into research on categories of software development knowledge, classifiers of textual content related to software engineering, and studies on the information needs of software developers.

9.1 Categorizing software development knowledge

Knowledge-based approaches have been extensively used in software development for decades Ding et al (2014), and many research efforts have been undertaken since the 1990s to categorize the kinds of knowledge relevant to software developers Erdös and Sneed (1998); Herbsleb and Kuwana (1993); Mylopoulos et al (1997).

More recently, Maalej and Robillard identified 12 types of knowledge contained in API documentation, with functionality and structure being the most prevalent Maalej and Robillard (2013). Because the authors focused on API documentation, the types of knowledge they identified are more technical than ours (e.g., containing API-specific concepts such as directives), however, there is some overlap with our categorization of GitHub README files (e.g., in categories such as ‘References’). Similar taxonomies have been developed by Monperrus et al. Monperrus et al (2012) and Jeong et al. Jeong et al (2009). Some of the guidelines identified by Jeong et al. apply to our work as well (e.g., “include ‘how to use’ documentation”) whereas other guidelines are specific to the domain of API documentation or to the user interface through which documentation is presented (e.g., “Effective Search”). Documentation in GitHub README files is broader than API documentation, and the documentation format and its presentation is at least partly specified by the GitHub markdown format.

In addition to API documentation, researchers have investigated the categories of knowledge contained in development blogs Pagano and Maalej (2013); Parnin and Treude (2011); Parnin et al (2013); Tiarks and Maalej (2014) and on Stack Overflow Asaduzzaman et al (2013); Nasehi et al (2012); Treude et al (2011). However, these formats serve different purposes compared to GitHub README files, and thus lead to different categories of software development knowledge.

9.2 Classifying software development text

The work most closely related to ours in terms of classifying the content of software documentation is OntoCat by Kumar and Devanbu Kumar and Devanbu (2016). Using Maalej and Robillard’s taxonomy of knowledge patterns in API documentation Maalej and Robillard (2013), they developed a domain independent technique to extract knowledge types from API reference documentation. Their system, OntoCat, uses nine different features and their semantic and statistical combinations to classify different knowledge types. Testing OntoCat on Python API documentation, the authors showed the effectiveness of their system. As described above, one major difference between work focused on API documentation and work on GitHub README files is that API documentation tends to be more technical. Similar to our work, Kumar and Devanbu also employed keyphrases for the classification, among other features. The F1 scores they report are in a similar range to the ones achieved by our classifier: Their weakest performance was for the categories of Non-Info (0.29) and Control Flow (0.31), while their strongest performance was for the categories of Code Examples (0.83) and Functionality and Behaviour (0.77). In our case, the lowest F1 scores were for the categories of ‘Other’ (0.303) and ‘Reference’ (0.605) while the highest scores were for ‘How’ (0.861) and ‘Contribution’ (0.814).

In other work focusing on automatically classifying the content of software documentation, Treude and Robillard developed a machine learning classifier that determines whether a sentence on Stack Overflow provides insight for a given API type 

Treude and Robillard (2016). Similarly, classifying content on Stack Overflow was the target of Campos et al. Campos and de Almeida Maia (2014) and de Souza et al.’s work de Souza et al (2014). Following on from Nasehi et al.’s categorization Nasehi et al (2012), they developed classifiers to identify questions belonging to different categories, such as ‘How-to-do-it’. Also using data from Stack Overflow, Correa and Sureka introduced a classifier to predict deleted questions Correa and Sureka (2014).

Text classification has also been applied to the information captured in other artifacts created by software developers, including change requests Antoniol et al (2008), development emails Sorbo et al (2015), code comments Pascarella and Bacchelli (2017), requirements specifications Mahmoud and Williams (2016), and app reviews Chen et al (2014); Guzman et al (2015); Kurtanović and Maalej (2017); Maalej et al (2016).

9.3 Information needs of software developers

Although there has not been much work on the information needs of software developers around GitHub repositories, there has been work on information needs of software developers in general. Early work focused mostly on program comprehension Erdem et al (1998); Johnson and Erdem (1997). Nykaza et al. investigated what learning support programmers need to successfully use a software development kit (SDK) Nykaza et al (2002), and they catalogued the content that was seen as necessary by their interviewees, including installation instructions and documentation of system requirements. There is some overlap with the codes that emerged from our analysis, but some of Nykaza et al.’s content suggestions are SDK-specific, such as “types of applications that can be developed with the SDK”.

Other studies on the information needs of software developers have analyzed newsgroup questions Hou et al (2005), questions in collocated development teams Ko et al (2007); Treude et al (2015), questions during software evolution tasks Sillito et al (2006, 2008), questions that focus on issues that occur within a project Fritz and Murphy (2010), questions that are hard to answer LaToza and Myers (2010), and information needs in software ecosystems Haenni et al (2013). The goal of Kirk et al.’s study was understanding problems that occur during framework reuse, and they identified four problems: understanding the functionality of framework components, understanding the interactions between framework components, understanding the mapping from the problem domain to the framework implementation, and understanding the architectural assumptions in the framework design Kirk et al (2007). These problems will arguably apply to frameworks hosted on GitHub, but not necessarily to other GitHub projects. Our categorization is broader by analyzing the content of GitHub README files for any type of software project. Future work might investigate README files that belong to particular kinds of projects.

10 Conclusions and Future Work

A README file is often the first document that a user sees when they encounter a new software repository. README files are essential in shaping the first impression of a repository and in documenting a software project. Despite their important role, we lack a systematic understanding of the content of README files as well as tools that can automate the discovery of relevant information contained in them.

In this paper, we have reported on a qualitative study which involved the manual annotation of 4,226 sections from 393 README files for repositories hosted on GitHub. We identified eight different kinds of content, and found that information regarding the ‘What’ and ‘How’ of a repository is common while information on the status of a project is rare. We then designed a classifier and a set of features to automatically predict the categories of sections in README files. Our classifier achieved an F1 score of 0.746 and we found that the most useful features for classifying the content of README files were often related to particular keywords.

Our findings provide a point of reference for repository owners against which they can model and evaluate their README files, ultimately leading to an improvement in the quality of software documentation. Our classifier will help automate these tasks and make it easier for users and owners of repositories to discover relevant information.

In addition to improving the precision and recall of our classifier, our future work lies in exploring the potential of the classifier to enable a more structured approach to searching and navigating GitHub README files. In particular, we plan to employ the classifier in a search interface for GitHub repositories and we will explore the feasibility of automatically reorganizing the documentation contained in GitHub README files using the structure that emerged from our qualitative analysis.


  • Agrawal et al (1993) Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the International Conference on Management of Data, ACM, New York, NY, USA, pp 207–216
  • Antoniol et al (2008) Antoniol G, Ayari K, Di Penta M, Khomh F, Guéhéneuc YG (2008) Is it a bug or an enhancement?: A text-based approach to classify change requests. In: Proceedings of the Conference of the Center for Advanced Studies on Collaborative Research: Meeting of Minds, ACM, New York, NY, USA, pp 23:304–23:318
  • Asaduzzaman et al (2013) Asaduzzaman M, Mashiyat AS, Roy CK, Schneider KA (2013) Answering questions about unanswered questions of Stack Overflow. In: Proceedings of the 10th Working Conference on Mining Software Repositories, IEEE Press, Piscataway, NJ, USA, pp 97–100
  • Begel et al (2013) Begel A, Bosch J, Storey MA (2013) Social networking meets software development: Perspectives from GitHub, MSDN, Stack Exchange, and TopCoder. IEEE Software 30(1):52–66
  • Bird et al (2009)

    Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc.

  • Campos and de Almeida Maia (2014) Campos EC, de Almeida Maia M (2014) Automatic categorization of questions from Q&A sites. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, ACM, New York, NY, USA, pp 641–643
  • Canfora et al (2013) Canfora G, De Lucia A, Di Penta M, Oliveto R, Panichella A, Panichella S (2013) Multi-objective cross-project defect prediction. In: Software Testing, Verification and Validation (ICST), 2013 IEEE Sixth International Conference on, IEEE, pp 252–261
  • Chen et al (2014) Chen N, Lin J, Hoi SCH, Xiao X, Zhang B (2014) Ar-miner: Mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th International Conference on Software Engineering, ACM, New York, NY, USA, pp 767–778
  • Corbin and Strauss (1990) Corbin JM, Strauss A (1990) Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology 13(1):3–21
  • Correa and Sureka (2014) Correa D, Sureka A (2014) Chaff from the wheat: Characterization and modeling of deleted questions on Stack Overflow. In: Proceedings of the 23rd International Conference on World Wide Web, ACM, New York, NY, USA, pp 631–642
  • Decan et al (2016) Decan A, Mens T, Claes M, Grosjean P (2016) When GitHub meets CRAN: An analysis of inter-repository package dependency problems. In: Proceedings of the 23rd International Conference on Software Analysis, Evolution, and Reengineering, IEEE, Piscataway, NJ, USA, pp 493–504
  • Ding et al (2014) Ding W, Liang P, Tang A, Van Vliet H (2014) Knowledge-based approaches in software documentation: A systematic literature review. Information and Software Technology 56(6):545–567
  • Erdem et al (1998) Erdem A, Johnson WL, Marsella S (1998) Task oriented software understanding. In: Proceedings of the 13th International Conference on Automated Software Engineering, IEEE Computer Society, Washington, DC, USA, pp 230–239
  • Erdös and Sneed (1998) Erdös K, Sneed HM (1998) Partial comprehension of complex programs (enough to perform maintenance). In: Proceedings of the 6th International Workshop on Program Comprehension, IEEE Computer Society, Washington, DC, USA, pp 98–105
  • Fogel (2005) Fogel K (2005) Producing Open Source Software: How to Run a Successful Free Software Project. O’Reilly Media, Inc., Sebastopol, CA, USA
  • Fritz and Murphy (2010) Fritz T, Murphy GC (2010) Using information fragments to answer the questions developers ask. In: Proceedings of the International Conference on Software Engineering - Volume 1, ACM, New York, NY, USA, pp 175–184
  • Greene and Fischer (2016) Greene GJ, Fischer B (2016) Cvexplorer: Identifying candidate developers by mining and exploring their open source contributions. In: Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, ACM, New York, NY, USA, pp 804–809
  • Guzman et al (2015) Guzman E, El-Haliby M, Bruegge B (2015) Ensemble methods for app review classification: An approach for software evolution (n). In: Proceedings of the 30th International Conference on Automated Software Engineering, IEEE Press, Piscataway, NJ, USA, pp 771–776
  • Haenni et al (2013) Haenni N, Lungu M, Schwarz N, Nierstrasz O (2013) Categorizing developer information needs in software ecosystems. In: Proceedings of the International Workshop on Ecosystem Architectures, ACM, New York, NY, USA, pp 1–5
  • Hassan and Wang (2017) Hassan F, Wang X (2017) Mining readme files to support automatic building of Java projects in software repositories: Poster. In: Proceedings of the 39th International Conference on Software Engineering Companion, IEEE Press, Piscataway, NJ, USA, pp 277–279
  • Hauff and Gousios (2015) Hauff C, Gousios G (2015) Matching GitHub developer profiles to job advertisements. In: Proceedings of the 12th Working Conference on Mining Software Repositories, IEEE Press, Piscataway, NJ, USA, pp 362–366
  • Herbsleb and Kuwana (1993) Herbsleb JD, Kuwana E (1993) Preserving knowledge in design projects: What designers need to know. In: Proceedings of the INTERACT ’93 and CHI ’93 Conference on Human Factors in Computing Systems, ACM, New York, NY, USA, pp 7–14
  • Hou et al (2005) Hou D, Wong K, Hoover HJ (2005) What can programmer questions tell us about frameworks? In: Proceedings of the 13th International Workshop on Program Comprehension, IEEE, Piscataway, NJ, USA, pp 87–96
  • Jeong et al (2009) Jeong SY, Xie Y, Beaton J, Myers BA, Stylos J, Ehret R, Karstens J, Efeoglu A, Busse DK (2009) Improving documentation for eSOA APIs through user studies. In: Proceedings of the 2nd International Symposium on End-User Development, Springer-Verlag, Berlin, Heidelberg, pp 86–105
  • Johnson and Erdem (1997) Johnson WL, Erdem A (1997) Interactive explanation of software systems. Automated Software Engineering 4(1):53–75
  • Kalliamvakou et al (2014) Kalliamvakou E, Gousios G, Blincoe K, Singer L, German DM, Damian D (2014) The promises and perils of mining GitHub. In: Proceedings of the 11th Working Conference on Mining Software Repositories, ACM, New York, NY, USA, pp 92–101
  • Kim et al (2008) Kim S, Whitehead Jr EJ, Zhang Y (2008) Classifying software changes: Clean or buggy? IEEE Transactions on Software Engineering 34(2):181–196
  • Kirk et al (2007) Kirk D, Roper M, Wood M (2007) Identifying and addressing problems in object-oriented framework reuse. Empirical Software Engineering 12(3):243–274
  • Ko et al (2007) Ko AJ, DeLine R, Venolia G (2007) Information needs in collocated software development teams. In: Proceedings of the 29th International Conference on Software Engineering, IEEE Computer Society, Washington, DC, USA, pp 344–353
  • Kumar and Devanbu (2016) Kumar N, Devanbu PT (2016) Ontocat: Automatically categorizing knowledge in API documentation. CoRR abs/1607.07602:preprint
  • Kurtanović and Maalej (2017) Kurtanović Z, Maalej W (2017) Mining user rationale from software reviews. In: Proceedings of the 25th International Requirements Engineering Conference, IEEE, Piscataway, NJ, USA, pp 61–70
  • LaToza and Myers (2010) LaToza TD, Myers BA (2010) Hard-to-answer questions about code. In: Evaluation and Usability of Programming Languages and Tools, ACM, New York, NY, USA, pp 8:1–8:6
  • Luaces et al (2012)

    Luaces O, Díez J, Barranquero J, del Coz JJ, Bahamonde A (2012) Binary relevance efficacy for multilabel classification. Progress in Artificial Intelligence 1(4):303–313

  • Maalej and Robillard (2013) Maalej W, Robillard MP (2013) Patterns of knowledge in API reference documentation. IEEE Transactions on Software Engineering 39(9):1264–1282
  • Maalej et al (2016) Maalej W, Kurtanović Z, Nabil H, Stanik C (2016) On the automatic classification of app reviews. Requirements Engineering 21(3):311–331
  • Mahmoud and Williams (2016) Mahmoud A, Williams G (2016) Detecting, classifying, and tracing non-functional software requirements. Requirements Engineering 21(3):357–381
  • Miles and Huberman (1994) Miles MB, Huberman AM (1994) Qualitative Data Analysis: An Expanded Sourcebook. SAGE publications
  • Monperrus et al (2012) Monperrus M, Eichberg M, Tekes E, Mezini M (2012) What should developers be aware of? an empirical study on the directives of api documentation. Empirical Software Engineering 17(6):703–737
  • Mylopoulos et al (1997) Mylopoulos J, Borgida A, Yu E (1997) Representing software engineering knowledge. Automated Software Engineering 4(3):291–317
  • Nam et al (2013) Nam J, Pan SJ, Kim S (2013) Transfer defect learning. In: Proceedings of the 2013 International Conference on Software Engineering, IEEE Press, pp 382–391
  • Nasehi et al (2012) Nasehi SM, Sillito J, Maurer F, Burns C (2012) What makes a good code example?: A study of programming Q&A in StackOverflow. In: Proceedings of the International Conference on Software Maintenance, IEEE Computer Society, Washington, DC, USA, pp 25–34
  • Nykaza et al (2002) Nykaza J, Messinger R, Boehme F, Norman CL, Mace M, Gordon M (2002) What programmers really want: Results of a needs assessment for sdk documentation. In: Proceedings of the 20th Annual International Conference on Computer Documentation, ACM, New York, NY, USA, pp 133–141
  • Pagano and Maalej (2013) Pagano D, Maalej W (2013) How do open source communities blog? Empirical Software Engineering 18(6):1090–1124
  • Panichella et al (2015) Panichella S, Di Sorbo A, Guzman E, Visaggio CA, Canfora G, Gall HC (2015) How can i improve my app? classifying user reviews for software maintenance and evolution. In: Software maintenance and evolution (ICSME), 2015 IEEE international conference on, IEEE, pp 281–290
  • Parnin and Treude (2011) Parnin C, Treude C (2011) Measuring API documentation on the web. In: Proceedings of the 2nd International Workshop on Web 2.0 for Software Engineering, ACM, New York, NY, USA, pp 25–30
  • Parnin et al (2013) Parnin C, Treude C, Storey MA (2013) Blogging developer knowledge: Motivations, challenges, and future directions. In: Proceedings of the 21st International Conference on Program Comprehension, IEEE Press, Piscataway, NJ, USA, pp 211–214
  • Pascarella and Bacchelli (2017) Pascarella L, Bacchelli A (2017) Classifying code comments in java open-source software systems. In: Proceedings of the 14th International Conference on Mining Software Repositories, IEEE Press, Piscataway, NJ, USA, pp 227–237
  • Pedregosa et al (2011) Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al (2011) Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12(Oct):2825–2830
  • Portugal and do Prado Leite (2016) Portugal RLQ, do Prado Leite JCS (2016) Extracting requirements patterns from software repositories. In: Proceedings of the 24th International Requirements Engineering Conference Workshops, IEEE, Piscataway, NJ, USA, pp 304–307
  • Rahman and Devanbu (2013) Rahman F, Devanbu P (2013) How, and why, process metrics are better. In: Proceedings of the 2013 International Conference on Software Engineering, IEEE Press, pp 432–441
  • Rahman et al (2012) Rahman F, Posnett D, Devanbu P (2012) Recalling the imprecision of cross-project defect prediction. In: Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering, ACM, pp 61:1–61:11
  • Sharma et al (2017) Sharma A, Thung F, Kochhar PS, Sulistya A, Lo D (2017) Cataloging GitHub repositories. In: Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, ACM, New York, NY, USA, pp 314–319
  • Sillito et al (2006) Sillito J, Murphy GC, De Volder K (2006) Questions programmers ask during software evolution tasks. In: Proceedings of the International Symposium on the Foundations of Software Engineering, ACM, New York, NY, USA, pp 23–34
  • Sillito et al (2008) Sillito J, Murphy GC, De Volder K (2008) Asking and answering questions during a programming change task. IEEE Transactions on Software Engineering 34(4):434–451
  • Sorbo et al (2015) Sorbo AD, Panichella S, Visaggio CA, Penta MD, Canfora G, Gall HC (2015) Development emails content analyzer: Intention mining in developer discussions (t). In: Proceedings of the 30th International Conference on Automated Software Engineering, IEEE Press, Piscataway, NJ, USA, pp 12–23
  • de Souza et al (2014) de Souza LBL, Campos EC, Maia MdA (2014) Ranking crowd knowledge to assist software development. In: Proceedings of the 22nd International Conference on Program Comprehension, ACM, New York, NY, USA, pp 72–82
  • Steinmacher et al (2016) Steinmacher I, Conte TU, Treude C, Gerosa MA (2016) Overcoming open source project entry barriers with a portal for newcomers. In: Proceedings of the 38th International Conference on Software Engineering, ACM, New York, NY, USA, pp 273–284
  • Tantithamthavorn et al (2017) Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2017) An empirical comparison of model validation techniques for defect prediction models. IEEE Transactions on Software Engineering 43(1):1–18
  • Tiarks and Maalej (2014) Tiarks R, Maalej W (2014) How does a typical tutorial for mobile development look like? In: Proceedings of the 11th Working Conference on Mining Software Repositories, ACM, New York, NY, USA, pp 272–281
  • Treude and Robillard (2016) Treude C, Robillard MP (2016) Augmenting API documentation with insights from Stack Overflow. In: Proceedings of the 38th International Conference on Software Engineering, ACM, New York, NY, USA, pp 392–403
  • Treude et al (2011) Treude C, Barzilay O, Storey MA (2011) How do programmers ask and answer questions on the web? (NIER track). In: Proceedings of the 33rd International Conference on Software Engineering, ACM, New York, NY, USA, pp 804–807
  • Treude et al (2015) Treude C, Figueira Filho F, Kulesza U (2015) Summarizing and measuring development activity. In: Proceedings of the 10th Joint Meeting on Foundations of Software Engineering, ACM, New York, NY, USA, pp 625–636
  • Zhang et al (2017) Zhang Y, Lo D, Kochhar PS, Xia X, Li Q, Sun J (2017) Detecting similar repositories on GitHub. In: Proceedings of the 24th International Conference on Software Analysis, Evolution and Reengineering, IEEE, Piscataway, NJ, USA, pp 13–23