4 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
BINGO! Simple Optimizers Win Big if Problems Collapse to a Few Buckets
Kishan Kumar Ganguly, Tim Menzies
Traditional multi-objective optimization in software engineering (SE) can be slow and complex. This paper introduces the BINGO effect: a novel phenomenon where SE data surprisingly…