314 citations · 779 across the 18 of their papers we have counts for
19 papers
Semantic Drift in Bug Resolution: How Behavioral Signals Propagate from Reports to Tests and Patches
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +7
Desc2Fix is a framework for measuring semantic alignment between bug reports, triggering tests, and developer-written fixes. Alignment is operationalized through structured behavio…
Humanizing Automatically Generated Unit Test Suites with LLM-Based Refactoring
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +7
Search-based test generation tools such as EvoSuite produce compilable and high-coverage unit tests at scale, but their suites are often hard to read and maintain. LLMs can generat…
Human-Aligned Code Readability Assessment with Large Language Models
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +6
Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-s…
Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
Large Language Models (LLMs) are increasingly used to refactor unit tests, improving readability and structure while preserving behavior. Evaluating such refactorings, however, rem…
Rethinking Cognitive Complexity for Unit Tests: Toward a Readability-Aware Metric Grounded in Developer Perception
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
Automatically generated unit tests-from search-based tools like EvoSuite or LLMs-vary significantly in structure and readability. Yet most evaluations rely on metrics like Cyclomat…
On the Diffusion of Test Smells in LLM-Generated Unit Tests
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
LLMs promise to transform unit test generation from a manual burden into an automated solution. Yet, beyond metrics such as compilability or coverage, little is known about the qua…
Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation
Wendkûuni C. Ouédraogo, Kader Kaboré, Yinghua Li +5
Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected. Search-based software testing improves efficiency but produces t…
The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
Haoye Tian, Kui Liu, Yinghua Li +7
A large body of the literature on automated program repair develops approaches where patches are automatically generated to be validated against an oracle (e.g., a test suite). Bec…
IBIR: Bug Report driven Fault Injection
Ahmed Khanfir, Anil Koyuncu, Mike Papadakis +4
Much research on software engineering and software testing relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-…
FlexiRepair: Transparent Program Repair with Generic Patches
Anil Koyuncu, Tegawendé F. Bissyandé, Jacques Klein +1
Template-based program repair research is in need for a common ground to express fix patterns in a standard and reusable manner. We propose to build on the concept of generic patch…
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Haoye Tian, Kui Liu, Abdoul Kader Kaboreé +4
A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an o…
On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs
Kui Liu, Shangwen Wang, Anil Koyuncu +7
Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test…
iFixR: Bug Report driven Program Repair
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé +4
Issue tracking systems are commonly used in modern software development for collecting feedback from users and developers. An ultimate automation target of software maintenance is…
TBar: Revisiting Template-based Automated Program Repair
Kui Liu, Anil Koyuncu, Dongsun Kim +1
We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps su…
D&C: A Divide-and-Conquer Approach to IR-based Bug Localization
Anil Koyuncu, Tegawendé F. Bissyandé, Dongsun Kim +4
Many automated tasks in software maintenance rely on information retrieval techniques to identify specific information within unstructured data. Bug localization is such a typical…
Impact of Tool Support in Patch Construction
Anil Koyuncu, Tegawendé F. Bissyandé, Dongsun Kim +3
In this work, we investigate the practice of patch construction in the Linux kernel development, focusing on the differences between three patching processes: (1) patches crafted e…
You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems
Kui Liu, Anil Koyuncu, Tegawendé F. Bissyandé +3
Properly benchmarking Automated Program Repair (APR) systems should contribute to the development and adoption of the research outputs by practitioners. To that end, the research c…
AVATAR : Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations
Kui Liu, Anil Koyuncu, Dongsun Kim +1
Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than…
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé +4
Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurr…