7 papers
Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE
Kishan Kumar Ganguly, Tim Menzies
The paper evaluates 20 optimizers on 106 software engineering tasks across different labeling budgets, showing that the best optimizer depends on the budget, and proposes a cheap l…
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…
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…
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…
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…
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…