237 citations · 2.5k across the 105 of their papers we have counts for
98 papers · 1 filter
Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics
Huy Tu, Tim Menzies
In many domains, there are many examples and far fewer labels for those examples; e.g. we may have access to millions of lines of source code, but access to only a handful of warni…
Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health
Andre Lustosa, Tim Menzies
When data is scarce, software analytics can make many mistakes. For example, consider learning predictors for open source project health (e.g. the number of closed pull requests in…
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
Suvodeep Majumder, Joymallya Chakraborty, Tim Menzies
Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training. Semi-supervised classifiers use…
Do I really need all this work to find vulnerabilities? An empirical case study comparing vulnerability detection techniques on a Java application
Sarah Elder, Nusrat Zahan, Rui Shu +4
CONTEXT: Applying vulnerability detection techniques is one of many tasks using the limited resources of a software project. OBJECTIVE: The goal of this research is to assist manag…
How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs
Rahul Yedida, Hong Jin Kang, Huy Tu +3
Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict…
AI-driven Development Is Here: Should You Worry?
Neil Ernst, Gabriele Bavota
AI-Driven Development Environments (AIDEs) Integrate the power of modern AI into IDEs like Visual Studio Code and JetBrains IntelliJ. By leveraging massive language models and the…
How to Improve Deep Learning for Software Analytics (a case study with code smell detection)
Rahul Yedida, Tim Menzies
To reduce technical debt and make code more maintainable, it is important to be able to warn programmers about code smells. State-of-the-art code small detectors use deep learners,…
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning
Huy Tu, Tim Menzies
Keeping track of and managing Self-Admitted Technical Debts (SATDs) is important for maintaining a healthy software project. Current active-learning SATD recognition tool involves…
What Not to Test (for Cyber-Physical Systems)
Xiao Ling, Tim Menzies
For simulation-based systems, finding a set of test cases with the least cost by exploring multiple goals is a complex task. Domain-specific optimization goals (e.g. maximize outpu…
PyTorrent: A Python Library Corpus for Large-scale Language Models
Mehdi Bahrami, N. C. Shrikanth, Shade Ruangwan +6
A large scale collection of both semantic and natural language resources is essential to leverage active Software Engineering research areas such as code reuse and code comprehensi…
SNEAK: Faster Interactive Search-based SE
Andre Lustosa, Jaydeep Patel, Venkata Sai Teja Malapati +1
When AI tools can generate many solutions, some human preference must be applied to determine which solution is relevant to the current project. One way to find those preferences i…
FRUGAL: Unlocking SSL for Software Analytics
Huy Tu, Tim Menzies
Standard software analytics often involves having a large amount of data with labels in order to commission models with acceptable performance. However, prior work has shown that s…
Crowdsourcing the State of the Art(ifacts)
Maria Teresa Baldassarre, Neil Ernst, Ben Hermann +2
In any field, finding the "leading edge" of research is an on-going challenge. Researchers cannot appease reviewers and educators cannot teach to the leading edge of their field if…
Fairer Software Made Easier (using "Keys")
Tim Menzies, Kewen Peng, Andre Lustosa
Can we simplify explanations for software analytics? Maybe. Recent results show that systems often exhibit a "keys effect"; i.e. a few key features control the rest. Just to say th…
Lessons learned from hyper-parameter tuning for microservice candidate identification
Rahul Yedida, Rahul Krishna, Anup Kalia +3
When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller *microservices*. While many tools have been proposed for this task, we warn…
Preference Discovery in Large Product Lines
Andre Lustosa, Tim Menzies
When AI tools can generate many solutions, some human preference must be applied to determine which solution is relevant to the current project. One way to find those preferences i…
VEER: Enhancing the Interpretability of Model-based Optimizations
Kewen Peng, Christian Kaltenecker, Norbert Siegmund +2
Many software systems can be tuned for multiple objectives (e.g., faster runtime, less required memory, less network traffic or energy consumption, etc.). Optimizers built for diff…
Assessing the Early Bird Heuristic (for Predicting Project Quality)
N. C. Shrikanth, Tim Menzies
Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical d…
Mining Scientific Workflows for Anomalous Data Transfers
Huy Tu, George Papadimitriou, Mariam Kiran +4
Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the workflow…
Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard
Sarah Elder, Nusrat Zahan, Val Kozarev +3
Lack of security expertise among software practitioners is a problem with many implications. First, there is a deficit of security professionals to meet current needs. Additionally…
Faster SAT Solving for Software with Repeated Structures (with Case Studies on Software Test Suite Minimization)
Jianfeng Chen, Xipeng Shen, Tim Menzies
Theorem provers has been used extensively in software engineering for software testing or verification. However, software is now so large and complex that additional architecture i…
Old but Gold: Reconsidering the value of feedforward learners for software analytics
Rahul Yedida, Xueqi Yang, Tim Menzies
There has been an increased interest in the use of deep learning approaches for software analytics tasks. State-of-the-art techniques leverage modern deep learning techniques such…
Early Life Cycle Software Defect Prediction. Why? How?
N. C. Shrikanth, Suvodeep Majumder, Tim Menzies
Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate th…
Making Fair ML Software using Trustworthy Explanation
Joymallya Chakraborty, Kewen Peng, Tim Menzies
Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having a huge social impact. But sometimes the behavior of this softwar…