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09/04/2023
Model Review: A PROMISEing Opportunity
To make models more understandable and correctable, I propose that the P...
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05/05/2023
On the Benefits of Semi-Supervised Test Case Generation for Cyber-Physical Systems
Testing complex Cyber Physical Systems (CPSs) can be expensive and time ...
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02/03/2023
Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics
In many domains, there are many examples and far fewer labels for those ...
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01/25/2023
Don't Lie to Me: Avoiding Malicious Explanations with STEALTH
STEALTH is a method for using some AI-generated model, without suffering...
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01/16/2023
Optimizing Predictions for Very Small Data Sets: a case study on Open-Source Project Health Prediction
When learning from very small data sets, the resulting models can make m...
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11/18/2022
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning
Deep neural networks (DNNs), are widely used in many industries such as ...
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11/10/2022
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
Labeling a module defective or non-defective is an expensive task. Hence...
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08/02/2022
Do I really need all this work to find vulnerabilities? An empirical case study comparing vulnerability detection techniques on a Java application
CONTEXT: Applying vulnerability detection techniques is one of many task...
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05/21/2022
How to Find Actionable Static Analysis Warnings
Automatically generated static code warnings suffer from a large number ...
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05/02/2022
Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning)
Background: Most of the existing machine learning models for security ta...
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03/22/2022
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue
Background: Machine learning techniques have been widely used and demons...
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02/02/2022
How to Improve Deep Learning for Software Analytics (a case study with code smell detection)
To reduce technical debt and make code more maintainable, it is importan...
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01/25/2022
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning
Keeping track of and managing Self-Admitted Technical Debts (SATDs) is i...
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12/02/2021
Faster Multi-Goal Simulation-Based Testing Using DoLesS (Domination with Least Square Approximation)
For cyber-physical systems, finding a set of test cases with the least c...
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11/03/2021
Can We Achieve Fairness Using Semi-Supervised Learning?
Ethical bias in machine learning models has become a matter of concern i...
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10/25/2021
Fair Enough: Searching for Sufficient Measures of Fairness
Testing machine learning software for ethical bias has become a pressing...
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10/06/2021
SNEAK: Faster Interactive Search-based Software Engineering (using Semi-Supervised Learning)
When reasoning over complex models, AI tools can generate too many solut...
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10/04/2021
PyTorrent: A Python Library Corpus for Large-scale Language Models
A large scale collection of both semantic and natural language resources...
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10/03/2021
xFAIR: Better Fairness via Model-based Rebalancing of Protected Attributes
Machine learning software can generate models that inappropriately discr...
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09/29/2021
Partitioning Cloud-based Microservices (via Deep Learning)
Cloud-based software has many advantages. When services are divided into...
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08/22/2021
FRUGAL: Unlocking SSL for Software Analytics
Standard software analytics often involves having a large amount of data...
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08/15/2021
Crowdsourcing the State of the Art(ifacts)
In any field, finding the "leading edge" of research is an on-going chal...
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07/11/2021
Fairer Software Made Easier (using "Keys")
Can we simplify explanations for software analytics? Maybe. Recent resul...
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06/12/2021
Lessons learned from hyper-parameter tuning for microservice candidate identification
When optimizing software for the cloud, monolithic applications need to ...
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06/07/2021
Preference Discovery in Large Product Lines
When AI tools can generate many solutions, some human preference must be...
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06/04/2021
VEER: Disagreement-Free Multi-objective Configuration
Software comes with many configuration options, satisfying varying needs...
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05/25/2021
Bias in Machine Learning Software: Why? How? What to do?
Increasingly, software is making autonomous decisions in case of crimina...
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05/24/2021
The Early Bird Catches the Worm: Better Early Life Cycle Defect Predictors
Before researchers rush to reason across all available data, they should...
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03/22/2021
Mining Scientific Workflows for Anomalous Data Transfers
Modern scientific workflows are data-driven and are often executed on di...
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03/08/2021
Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard
Lack of security expertise among software practitioners is a problem wit...
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01/15/2021
When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time
Is deep learning over-hyped? Where are the case studies that compare sta...
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01/08/2021
Faster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization)
Theorem provers has been used extensively in software engineering for so...
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11/26/2020
Early Life Cycle Software Defect Prediction. Why? How?
Many researchers assume that, for software analytics, "more data is bett...
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11/23/2020
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
BACKGROUND: Machine learning-based security detection models have become...
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10/07/2020
Empirical Standards for Software Engineering Research
Empirical Standards are natural-language models of a scientific communit...
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08/21/2020
Revisiting Process versus Product Metrics: a Large Scale Analysis
Numerous methods can build predictive models from software data. But wha...
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08/14/2020
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?
How to make software analytics simpler and faster? One method is to matc...
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08/09/2020
Improving Deep Learning for Defect Prediction (using the GHOST Hyperparameter Optimizer)
There has been much recent interest in the application of deep learning ...
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08/03/2020
How Different is Test Case Prioritization for Open and Closed Source Projects?
Improved test case prioritization means that software developers can det...
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07/06/2020
Making Fair ML Software using Trustworthy Explanation
Machine learning software is being used in many applications (finance, h...
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06/12/2020
Defect Reduction Planning (using TimeLIME)
Software comes in releases. An implausible change to software is somethi...
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06/12/2020
Predicting Project Health for Open Source Projects (using the DECART Hyperparameter Optimizer)
Software developed on public platforms are a source of data that can be ...
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06/09/2020
Assessing Practitioner Beliefs about Software Engineering
Software engineering is a highly dynamic discipline. Hence, as times cha...
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05/31/2020
How to Recognize Actionable Static Code Warnings (Using Linear SVMs)
Static code warning tools often generate warnings that programmers ignor...
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03/23/2020
Fairway: A Way to Build Fair ML Software
Machine learning software is increasingly being used to make decisions t...
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03/15/2020
How to Improve AI Tools (by Adding in SE Knowledge): Experiments with the TimeLIME Defect Reduction Tool
AI algorithms are being used with increased frequency in SE research and...
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03/12/2020
The Changing Nature of Computational Science Software
How should software engineering be adapted for Computational Science (CS...
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02/25/2020
Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach
Keeping track of and managing the self-admitted technical debts (SATDs) ...
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12/20/2019
Assessing Practitioner Beliefs about Software Defect Prediction
Just because software developers say they believe in "X", that does not ...
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12/20/2019