450 citations · 883 across the 31 of their papers we have counts for
31 papers
Synthetic Semantic Supervision for Contrastive Code Representation Learning in Small Transformers: An Empirical Study
Kenneth Paulsen, Florian Tambon, Mike Papadakis +1
General-purpose code embeddings power tools for code search, classification, and retrieval. Compact transformer encoders for code typically rely on either human-written docstrings…
Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap
Naryeong Kim, Shin Yoo
Open-weight Small Language Models(SLMs) can provide faster local inference at lower financial cost, but may not achieve the same performance level as commercial Large Language Mode…
Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs
Juyeon Yoon, Somin Kim, Robert Feldt +1
Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program analysis and generation. Yet testing them…
Identifying Bug Inducing Commits by Combining Fault Localisation and Code Change Histories
Gabin An, Jinsu Choi, Jingun Hong +2
A Bug Inducing Commit (BIC) is a code change that introduces a bug into the codebase. Although the abnormal or unexpected behavior caused by the bug may not manifest immediately, i…
COSMosFL: Ensemble of Small Language Models for Fault Localisation
Hyunjoon Cho, Sungmin Kang, Gabin An +1
LLMs are rapidly being adopted to build powerful tools and agents for software engineering, but most of them rely heavily on extremely large closed-source models. This, in turn, ca…
Real Faults in Deep Learning Fault Benchmarks: How Real Are They?
Gunel Jahangirova, Nargiz Humbatova, Jinhan Kim +2
As the adoption of Deep Learning (DL) systems continues to rise, an increasing number of approaches are being proposed to test these systems, localise faults within them, and repai…
An Empirical Study of Fault Localisation Techniques for Deep Learning
Nargiz Humbatova, Jinhan Kim, Gunel Jahangirova +2
With the increased popularity of Deep Neural Networks (DNNs), increases also the need for tools to assist developers in the DNN implementation, testing and debugging process. Sever…
CSA-Trans: Code Structure Aware Transformer for AST
Saeyoon Oh, Shin Yoo
When applying the Transformer architecture to source code, designing a good self-attention mechanism is critical as it affects how node relationship is extracted from the Abstract…
Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing
Juyeon Yoon, Robert Feldt, Shin Yoo
GUI testing checks if a software system behaves as expected when users interact with its graphical interface, e.g., testing specific functionality or validating relevant use case s…
Large Language Models for Software Engineering: Survey and Open Problems
Angela Fan, Beliz Gokkaya, Mark Harman +4
This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LL…
Evaluating Diverse Large Language Models for Automatic and General Bug Reproduction
Sungmin Kang, Juyeon Yoon, Nargiz Askarbekkyzy +1
Bug reproduction is a critical developer activity that is also challenging to automate, as bug reports are often in natural language and thus can be difficult to transform to test…
Just-in-Time Flaky Test Detection via Abstracted Failure Symptom Matching
Gabin An, Juyeon Yoon, Thomas Bach +2
We report our experience of using failure symptoms, such as error messages or stack traces, to identify flaky test failures in a Continuous Integration (CI) pipeline for a large in…
The GitHub Recent Bugs Dataset for Evaluating LLM-based Debugging Applications
Jae Yong Lee, Sungmin Kang, Juyeon Yoon +1
Large Language Models (LLMs) have demonstrated strong natural language processing and code synthesis capabilities, which has led to their rapid adoption in software engineering app…
A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
Sungmin Kang, Gabin An, Shin Yoo
Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce dev…
Towards Autonomous Testing Agents via Conversational Large Language Models
Robert Feldt, Sungmin Kang, Juyeon Yoon +1
Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveri…
Towards Objective-Tailored Genetic Improvement Through Large Language Models
Sungmin Kang, Shin Yoo
While Genetic Improvement (GI) is a useful paradigm to improve functional and nonfunctional aspects of software, existing techniques tended to use the same set of mutation operator…
The Inversive Relationship Between Bugs and Patches: An Empirical Study
Jinhan Kim, Jongchan Park, Shin Yoo
Software bugs pose an ever-present concern for developers, and patching such bugs requires a considerable amount of costs through complex operations. In contrast, introducing bugs…
Repairing DNN Architecture: Are We There Yet?
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova +2
As Deep Neural Networks (DNNs) are rapidly being adopted within large software systems, software developers are increasingly required to design, train, and deploy such models into…
A Bayesian Framework for Automated Debugging
Sungmin Kang, Wonkeun Choi, Shin Yoo
Debugging takes up a significant portion of developer time. As a result, automated debugging techniques including Fault Localization (FL) and Automated Program Repair (APR) have ga…
Fonte: Finding Bug Inducing Commits from Failures
Gabin An, Jingun Hong, Naryeong Kim +1
A Bug Inducing Commit (BIC) is a commit that introduces a software bug into the codebase. Knowing the relevant BIC for a given bug can provide valuable information for debugging as…
Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction
Sungmin Kang, Juyeon Yoon, Shin Yoo
Many automated test generation techniques have been developed to aid developers with writing tests. To facilitate full automation, most existing techniques aim to either increase c…
Effectively Sampling Higher Order Mutants Using Causal Effect
Saeyoon Oh, Seongmin Lee, Shin Yoo
Higher Order Mutation (HOM) has been proposed to avoid equivalent mutants and improve the scalability of mutation testing, but generating useful HOMs remain an expensive search pro…
Predictive Mutation Analysis via Natural Language Channel in Source Code
Jinhan Kim, Juyoung Jeon, Shin Hong +1
Mutation analysis can provide valuable insights into both System Under Test (SUT) and its test suite. However, it is not scalable due to the cost of building and testing a large nu…
FDG: A Precise Measurement of Fault Diagnosability Gain of Test Cases
Gabin An, Shin Yoo
The performance of many Fault Localisation (FL) techniques directly depends on the quality of the used test suites. Consequently, it is extremely useful to be able to precisely mea…