Shin Hwei Tan

Shin Hwei Tan, Chunfeng Hu, Ziqiang Li et al.

Many studies have shown the benefits of introducing open-source projects into teaching Software Engineering (SE) courses. However, there are several limitations of existing studies that limit the wide adaptation of open-source projects in a classroom setting, including (1) the selected project is limited to one particular project, (2) most studies only investigated on its effect on teaching a specific SE concept, and (3) students may make mistakes in their contribution which leads to poor quality code. Meanwhile, software companies have successfully launched programs like Google Summer of Code (GSoC) and FindBugs "fixit" to contribute to open-source projects. Inspired by the success of these programs, we propose GitHub-OSS Fixit, a course project where students are taught to contribute to open-source Java projects by fixing bugs reported in GitHub. We described our course outline to teach students SE concepts by encouraging the usages of several automated program analysis tools. We also included the carefully designed instructions that we gave to students for participating in GitHub-OSS Fixit. As all lectures and labs are conducted online, we think that our course design could help in guiding future online SE courses. Overall, our survey results show that students think that GitHub-OSS Fixit could help them to improve many skills and apply the knowledge taught in class. In total, 154 students have submitted 214 pull requests to 24 different Java projects, in which 59 of them have been merged, and 82 have been closed by developers.

Michael R. Lyu, Baishakhi Ray, Abhik Roychoudhury et al.

Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance

Junjielong Xu, Ying Fu, Shin Hwei Tan et al.

Large language models (LLMs) have achieved decent results on automated program repair (APR). However, the next token prediction training objective of decoder-only LLMs (e.g., GPT-4) is misaligned with the masked span prediction objective of current infilling-style methods, which impedes LLMs from fully leveraging pre-trained knowledge for program repair. In addition, while some LLMs can locate and repair bugs in certain functions using the related artifacts (e.g., test cases), existing methods still depend on statement-level fault localization methods to provide a list of buggy hunks for repair. This restriction hinders LLMs from exploring potential patches beyond the given locations. In this paper, we investigate a new approach to adapt LLMs to program repair. Our core insight is that LLM's APR capability can be greatly improved by simply aligning the output to their training objective and allowing them to refine the whole program without first identifying faulty statements. Based on this insight, we designed D4C, a straightforward prompting framework for APR. D4C can repair 180 bugs correctly in Defects4J, with each patch being sampled only 10 times. This surpasses the SOTA APR methods with perfect fault localization by 10% and reduces the patch sampling number by 90%. Our findings reveal that (1) objective alignment is crucial for fully exploiting LLM's pre-trained capability, and (2) replacing the traditional localize-buggy-hunks-then-repair workflow with direct debugging is more effective for LLM-based APR methods. Thus, we believe this paper introduces a new mindset for harnessing LLMs in APR.

Guixin Ye, Zhanyong Tang, Shin Hwei Tan et al.

JavaScript (JS) is a popular, platform-independent programming language. To ensure the interoperability of JS programs across different platforms, the implementation of a JS engine should conform to the ECMAScript standard. However, doing so is challenging as there are many subtle definitions of API behaviors, and the definitions keep evolving. We present COMFORT, a new compiler fuzzing framework for detecting JS engine bugs and behaviors that deviate from the ECMAScript standard. COMFORT leverages the recent advance in deep learning-based language models to automatically generate JS test code. As a departure from prior fuzzers, COMFORT utilizes the well-structured ECMAScript specifications to automatically generate test data along with the test programs to expose bugs that could be overlooked by the developers or manually written test cases. COMFORT then applies differential testing methodologies on the generated test cases to expose standard conformance bugs. We apply COMFORT to ten mainstream JS engines. In 200 hours of automated concurrent testing runs, we discover bugs in all tested JS engines. We had identified 158 unique JS engine bugs, of which 129 have been verified, and 115 have already been fixed by the developers. Furthermore, 21 of the Comfort-generated test cases have been added to Test262, the official ECMAScript conformance test suite.

Shin Hwei Tan, Ziqiang Li, Lu Yan

Many automated program repair techniques have been proposed for fixing bugs. Some of these techniques use the information beyond the given buggy program and test suite to improve the quality of generated patches. However, there are several limitations that hinder the wide adoption of these techniques, including (1) they rely on a fixed set of repair templates for patch generation or reference implementation, (2) searching for the suitable reference implementation is challenging, (3) generated patches are not explainable. Meanwhile, a recent approach shows that similar bugs exist across different projects and one could use the GitHub issue from a different project for finding new bugs for a related project. We propose collaborative bug fixing, a novelapproach that suggests bug reports that describe a similar bug. Our studyredefines similar bugs as bugs that share the (1) same libraries, (2) same functionalities, (3) same reproduction steps, (4) same configurations, (5) sameoutcomes, or (6) same errors. Moreover, our study revealed the usefulness of similar bugs in helping developers in finding more context about the bug and fixing. Based on our study, we design CrossFix, a tool that automatically suggests relevant GitHub issues based on an open GitHub issue. Our evaluation on 249 open issues from Java and Android projects shows that CrossFix could suggest similar bugs to help developers in debugging and fixing.

Sergey Mechtaev, Xiang Gao, Shin Hwei Tan et al.

Automated program repair is a problem of finding a transformation (called a patch) of a given incorrect program that eliminates the observable failures. It has important applications such as providing debugging aids, automatically grading assignments and patching security vulnerabilities. A common challenge faced by all existing repair techniques is scalability to large patch spaces, since there are many candidate patches that these techniques explicitly or implicitly consider. The correctness criterion for program repair is often given as a suite of tests, since a formal specification of the intended program behavior may not be available. Current repair techniques do not scale due to the large number of test executions performed by the underlying search algorithms. We address this problem by introducing a methodology of patch generation based on a test-equivalence relation (if two programs are "test-equivalent" for a given test, they produce indistinguishable results on this test). We propose two test-equivalence relations based on runtime values and dependencies respectively and present an algorithm that performs on-the-fly partitioning of patches into test-equivalence classes. Our experiments on real-world programs reveal that the proposed methodology drastically reduces the number of test executions and therefore provides an order of magnitude efficiency improvement over existing repair techniques, without sacrificing patch quality.

Shin Hwei Tan, Darko Marinov, Lin Tan et al.

This paper has been withdrawn by the author.