From the 1 of 9 linked papers with an AI index.
9 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…
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…
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…
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…