collaborators

5 papers

cs.SE2026

On the Reliability of Code Comprehension Proxies

Erfan Arvan, Nadeeshan De Silva, Oscar Chaparro +1

Prior work on code comprehension uses different comprehension proxies-for example, Likert-scale ratings or answers to input-output questions about program snippets, usually collect…

cs.SE2026

Verifier Warnings Do Not Improve Comprehensibility Prediction

Nadeeshan De Silva, Martin Kellogg, Oscar Chaparro

Proponents of software verification suggest that code simplicity is linked to the effort to verify code, hypothesizing that formal verifiers produce fewer false positive warnings a…

cs.SE2026

Recovering Fine-Grained Code Change Rationale from Multiple Software Artifacts

Mehedi Sun, Antu Saha, Nadeeshan De Silva +2

Understanding the reasons behind past code changes is critical for refactoring, code review, and debugging. However, code change rationale is often fragmented, inconsistently docum…

cs.SE2025

From Absolute to Relative Code Comprehensibility Prediction

Nadeeshan De Silva, Martin Kellogg, Oscar Chaparro

Automatically predicting code comprehensibility could support tasks such as refactoring and code review. Existing metrics correlate poorly with human comprehension, motivating ML m…

cs.SE2025

LadyBug: A GitHub Bot for UI-Enhanced Bug Localization in Mobile Apps

Junayed Mahmud, James Chen, Terry Achille +8

This paper introduces LadyBug, a GitHub bot that automatically localizes bugs for Android apps by combining UI interaction information with text retrieval. LadyBug connects to an A…