314 citations · 761 across the 17 of their papers we have counts for
17 papers
How are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective
Kisub Kim, Xin Zhou, Dongsun Kim +6
Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication…
Towards More Realistic Evaluation for Neural Test Oracle Generation
Zhongxin Liu, Kui Liu, Xin Xia +1
Effective unit tests can help guard and improve software quality but require a substantial amount of time and effort to write and maintain. A unit test consists of a test prefix an…
App Review Driven Collaborative Bug Finding
Xunzhu Tang, Haoye Tian, Pingfan Kong +3
Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., t…
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Haoye Tian, Xunzhu Tang, Andrew Habib +5
In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour.…
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…
Characterizing Sensor Leaks in Android Apps
Xiaoyu Sun, Xiao Chen, Kui Liu +3
While extremely valuable to achieve advanced functions, mobile phone sensors can be abused by attackers to implement malicious activities in Android apps, as experimentally demonst…
Beep: Fine-grained Fix Localization by Learning to Predict Buggy Code Elements
Shangwen Wang, Kui Liu, Bo Lin +4
Software Fault Localization refers to the activity of finding code elements (e.g., statements) that are related to a software failure. The state-of-the-art fault localization techn…
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Haoye Tian, Yinghua Li, Weiguo Pian +5
Towards predicting patch correctness in APR, we propose a simple, but novel hypothesis on how the link between the patch behaviour and failing test specifications can be drawn: sim…
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…
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…
Mining Fix Patterns for FindBugs Violations
Kui Liu, Dongsun Kim, Tegawendé F. Bissyandé +2
In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in…