9 papers
Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE
Kishan Kumar Ganguly, Tim Menzies
Configuring and tuning modern software is unavoidable, expensive, and error-prone: a single system can expose hundreds of interacting options, and scoring one setting can mean a fu…
Is Model Instability just Noise to be Tolerated or a Property that can be Managed?
Amirali Rayegan, Lunxiao Li, Tim Menzies
In software analytics, rerunning the same analysis twice often yields different models and conclusions. This reduces trust in the model and limits its use. We find that model insta…
Zoom, Don't Wander: Why Regional Search Outperforms Pareto Reasoning and Global Optimization in Budget-Constrained SBSE
Kishan Kumar Ganguly, Tim Menzies
Traditional Search-Based Software Engineering (SBSE) assumes global search and full Pareto exploration are essential. We offer the following negative result based on a study of ove…
From Verification to Herding: Exploiting Software's Sparsity of Influence
Tim Menzies, Kishan Kumar Ganguly
Software verification is now costly, taking over half the project effort while failing on modern complex systems. We hence propose a shift from verification and modeling to herding…
Can Causality Cure Confusion Caused By Correlation (in Software Analytics)?
Amirali Rayegan, Tim Menzies
Background: Symbolic models, particularly decision trees, are widely used in software engineering for explainable analytics in defect prediction, configuration tuning, and software…
From Coverage to Causes: Data-Centric Fuzzing for JavaScript Engines
Kishan Kumar Ganguly, Tim Menzies
Context: Exhaustive fuzzing of modern JavaScript engines is infeasible due to the vast number of program states and execution paths. Coverage-guided fuzzers waste effort on low-ris…
How Low Can You Go? The Data-Light SE Challenge
Kishan Kumar Ganguly, Tim Menzies
Much of Software Engineering (SE) research assumes that progress depends on massive datasets and CPU-intensive optimizers. Yet has this assumption been rigorously tested? The count…
MOOT: a Repository of Many Multi-Objective Optimization Tasks
Tim Menzies, Tao Chen, Yulong Ye +4
Software engineers must make decisions that trade off competing goals (faster vs. cheaper, secure vs. usable, accurate vs. interpretable, etc.). Despite MSR's proven techniques for…
Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization
Amirali Rayegan, Tim Menzies
Efficient, interpretable optimization is a critical but underexplored challenge in software engineering, where practitioners routinely face vast configuration spaces and costly, er…