28 citations · 54 across the 13 of their papers we have counts for
15 papers
Covered Is Not Checked: Which Branch Outcomes Would Your Tests Notice?
Md Nafiu Rahman, Cemil Canbaz, Soneya Binta Hossain
Code coverage reports what tests execute, not whether tests detect faults. Checked coverage addresses this gap by tracing code that influences test oracles through dynamic slicing,…
Are We Testing the Wrong Quantum Bugs? A Study of Fault Representation Gaps in Quantum Software Testing
Asif Mohammed Saad, Ahsanul Ameen Sabit, Jonah Brandenburgh +3
Quantum software relies on compilers, intermediate representations, and execution frameworks that transform programs before execution. Faults in these components can change the beh…
Documentation vs. Code Patterns: What Drives LLM-Based Exception Oracle Generation?
Soneya Binta Hossain, Matthew B. Dwyer, Tasfia Tasnim
LLM-based test oracle generation (TOG) methods report high accuracy on exception oracle generation, but it remains unclear what evidence drives these predictions. In particular, do…
PROGRESS: Property-Guided Regression Search for Semantic Falsification
Davis Tocheuk Mo, Noshin Ulfat, Matthew B. Dwyer +1
Search-based regression-test generation effectively explores complex program structures, yielding high structural coverage, but its oracles are derived from the system under test:…
CoCoMUT: A Tool for Code-Context Mining and Automated Dataset Generation
Alessandro Botta, Shiven Garisa, Jaya Vardhini Akurathi +3
Software-engineering assistants often need method-level context beyond an isolated body, including enclosing-class information, documentation, callers, callees, type hierarchy, and…
PITMuS: A Tool for Automated Bug Dataset Generation via Source-Level Mutant Reconstruction
Tasfia Tasnim, Soneya Binta Hossain
Mutation testing provides a scalable mechanism for generating controlled faults for software testing and empirical software engineering. In Java, PIT is a widely used mutation test…
From Threads to Trajectories: A Multi-LLM Pipeline for Community Knowledge Extraction from GitHub Issue Discussions
Nazia Shehnaz Joynab, Soneya Binta Hossain
Resolution of complex post-production issues in large-scale open-source software (OSS) projects requires significant cognitive effort, as developers need to go through long, unstru…
Measuring LLM Trust Allocation Across Conflicting Software Artifacts
Noshin Ulfat, Ahsanul Ameen Sabit, Soneya Binta Hossain
LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mu…
Doc2OracLL: Investigating the Impact of Documentation on LLM-based Test Oracle Generation
Soneya Binta Hossain, Raygan Taylor, Matthew Dwyer
Code documentation is a critical aspect of software development, serving as a bridge between human understanding and machine-readable code. Beyond assisting developers in understan…
A Deep Dive into Large Language Models for Automated Bug Localization and Repair
Soneya Binta Hossain, Nan Jiang, Qiang Zhou +5
Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR). In this study, we take a deep dive…
LeDex: Training LLMs to Better Self-Debug and Explain Code
Nan Jiang, Xiaopeng Li, Shiqi Wang +6
In the domain of code generation, self-debugging is crucial. It allows LLMs to refine their generated code based on execution feedback. This is particularly important because gener…
TOGLL: Correct and Strong Test Oracle Generation with LLMs
Soneya Binta Hossain, Matthew Dwyer
Test oracles play a crucial role in software testing, enabling effective bug detection. Despite initial promise, neural-based methods for automated test oracle generation often res…
Neural-Based Test Oracle Generation: A Large-scale Evaluation and Lessons Learned
Soneya Binta Hossain, Antonio Filieri, Matthew B. Dwyer +2
Defining test oracles is crucial and central to test development, but manual construction of oracles is expensive. While recent neural-based automated test oracle generation techni…
Artifact: Measuring and Mitigating Gaps in Structural Testing
Soneya Binta Hossain, Matthew B. Dwyer, Sebastian Elbaum +1
The artifact used for evaluating the experimental results of Measuring and Mitigating Gaps in Structural Testing is publicly available on GitHub, Software Heritage and figshare, an…
A Brief Survey on Oracle-based Test Adequacy Metrics
Soneya Binta Hossain, Matthew B. Dwyer
Code coverage is a popular and widespread test adequacy metric that measures the percentage of program codes executed by a test suite. Despite its popularity, code coverage has sev…