b'Tim Menzies'

research

∙ 09/04/2023

Model Review: A PROMISEing Opportunity

To make models more understandable and correctable, I propose that the P...

0 Tim Menzies, et al. ∙

research

∙ 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 ...

0 Xiao Ling, et al. ∙

research

∙ 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 ...

0 Huy Tu, et al. ∙

research

∙ 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...

0 Lauren Alvarez, et al. ∙

research

∙ 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...

0 Andre Lustosa, et al. ∙

research

∙ 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 ...

0 Guanqin Zhang, et al. ∙

research

∙ 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...

0 Suvodeep Majumder, et al. ∙

research

∙ 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...

0 Sarah Elder, et al. ∙

research

∙ 05/21/2022

How to Find Actionable Static Analysis Warnings

Automatically generated static code warnings suffer from a large number ...

0 Rahul Yedida, et al. ∙

research

∙ 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...

0 Rui Shu, et al. ∙

research

∙ 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...

0 Rui Shu, et al. ∙

research

∙ 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...

0 Rahul Yedida, et al. ∙

research

∙ 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...

5 Huy Tu, et al. ∙

research

∙ 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...

0 Xiao Ling, et al. ∙

research

∙ 11/03/2021

Can We Achieve Fairness Using Semi-Supervised Learning?

Ethical bias in machine learning models has become a matter of concern i...

0 Joymallya Chakraborty, et al. ∙

research

∙ 10/25/2021

Fair Enough: Searching for Sufficient Measures of Fairness

Testing machine learning software for ethical bias has become a pressing...

0 Suvodeep Majumder, et al. ∙

research

∙ 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...

0 Andre Lustosa, et al. ∙

research

∙ 10/04/2021

PyTorrent: A Python Library Corpus for Large-scale Language Models

A large scale collection of both semantic and natural language resources...

0 Mehdi Bahrami, et al. ∙

research

∙ 10/03/2021

xFAIR: Better Fairness via Model-based Rebalancing of Protected Attributes

Machine learning software can generate models that inappropriately discr...

0 Kewen Peng, et al. ∙

research

∙ 09/29/2021

Partitioning Cloud-based Microservices (via Deep Learning)

Cloud-based software has many advantages. When services are divided into...

0 Rahul Yedida, et al. ∙

research

∙ 08/22/2021

FRUGAL: Unlocking SSL for Software Analytics

Standard software analytics often involves having a large amount of data...

0 Huy Tu, et al. ∙

research

∙ 08/15/2021

Crowdsourcing the State of the Art(ifacts)

In any field, finding the "leading edge" of research is an on-going chal...

0 Maria Teresa Baldassarre, et al. ∙

research

∙ 07/11/2021

Fairer Software Made Easier (using "Keys")

Can we simplify explanations for software analytics? Maybe. Recent resul...

0 Tim Menzies, et al. ∙

research

∙ 06/12/2021

Lessons learned from hyper-parameter tuning for microservice candidate identification

When optimizing software for the cloud, monolithic applications need to ...

0 Rahul Yedida, et al. ∙

research

∙ 06/07/2021

Preference Discovery in Large Product Lines

When AI tools can generate many solutions, some human preference must be...

0 Andre Lustosa, et al. ∙

research

∙ 06/04/2021

VEER: Disagreement-Free Multi-objective Configuration

Software comes with many configuration options, satisfying varying needs...

0 Kewen Peng, et al. ∙

research

∙ 05/25/2021

Bias in Machine Learning Software: Why? How? What to do?

Increasingly, software is making autonomous decisions in case of crimina...

0 Joymallya Chakraborty, et al. ∙

research

∙ 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...

0 N. C. Shrikanth, et al. ∙

research

∙ 03/22/2021

Mining Scientific Workflows for Anomalous Data Transfers

Modern scientific workflows are data-driven and are often executed on di...

0 Huy Tu, et al. ∙

research

∙ 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...

0 Sarah Elder, et al. ∙

research

∙ 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...

0 Rahul Yedida, et al. ∙

research

∙ 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...

0 Jianfeng Chen, et al. ∙

research

∙ 11/26/2020

Early Life Cycle Software Defect Prediction. Why? How?

Many researchers assume that, for software analytics, "more data is bett...

0 N. C. Shrikanth, et al. ∙

research

∙ 11/23/2020

Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack

BACKGROUND: Machine learning-based security detection models have become...

0 Rui Shu, et al. ∙

research

∙ 10/07/2020

Empirical Standards for Software Engineering Research

Empirical Standards are natural-language models of a scientific communit...

0 Paul Ralph, et al. ∙

research

∙ 08/21/2020

Revisiting Process versus Product Metrics: a Large Scale Analysis

Numerous methods can build predictive models from software data. But wha...

0 Suvodeep Majumder, et al. ∙

research

∙ 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...

0 Amritanshu Agrawal, et al. ∙

research

∙ 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 ...

0 Rahul Yedida, et al. ∙

research

∙ 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...

0 Xiao Ling, et al. ∙

research

∙ 07/06/2020

Making Fair ML Software using Trustworthy Explanation

Machine learning software is being used in many applications (finance, h...

0 Joymallya Chakraborty, et al. ∙

research

∙ 06/12/2020

Defect Reduction Planning (using TimeLIME)

Software comes in releases. An implausible change to software is somethi...

0 Kewen Peng, et al. ∙

research

∙ 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 ...

0 Tianpei Xia, et al. ∙

research

∙ 06/09/2020

Assessing Practitioner Beliefs about Software Engineering

Software engineering is a highly dynamic discipline. Hence, as times cha...

0 Shrikanth NC, et al. ∙

research

∙ 05/31/2020

How to Recognize Actionable Static Code Warnings (Using Linear SVMs)

Static code warning tools often generate warnings that programmers ignor...

0 Xueqi Yang, et al. ∙

research

∙ 03/23/2020

Fairway: A Way to Build Fair ML Software

Machine learning software is increasingly being used to make decisions t...

0 Joymallya Chakraborty, et al. ∙

research

∙ 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...

0 Kewen Peng, et al. ∙

research

∙ 03/12/2020

The Changing Nature of Computational Science Software

How should software engineering be adapted for Computational Science (CS...

0 Huy Tu, et al. ∙

research

∙ 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) ...

0 Zhe Yu, et al. ∙

research

∙ 12/20/2019

Assessing Practitioner Beliefs about Software Defect Prediction

Just because software developers say they believe in "X", that does not ...

0 N. C. Shrikanth, et al. ∙

research

∙ 12/20/2019

Assessing Practitioner Beliefs

Just because software developers say they believe in "X" that does not n...

0 N. C. Shrikanth, et al. ∙

Tim Menzies

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