Jiakun Liu

Dawei Tian, Jiakun Liu, Yun Peng et al.

Modern software systems evolve rapidly under CI/CD practices, where tests are critical for quality. However, substantial code changes often render existing test cases obsolete, causing pipeline disruptions, reduced productivity, and compromised quality. Recent automatic test update approaches leverage LLMs to refine test cases via execution feedback and exact-matching context retrieval, prioritizing executability and line coverage but suffering three limitations: (1) neglecting test assertion adequacy, weakening fault detection; (2) relying on coarse line coverage instead of specific uncovered lines/branches; (3) using exact-matching retrieval, which fails for LLM hallucinated queries. To address these, we propose MuMuTestUp, a mutation-guided multi-agent framework with three specialized agents: Mutation Analysis (strengthens assertions via surviving mutants), Coverage Analysis (generates targeted repair instructions for uncovered lines/branches), and Semantic Retrieval (handles hallucinations via semantic-similarity search). We also construct PRBENCH, a 571-sample pull-request-level dataset from 10 open-source Java projects (validated for cross-commit update scenarios). Evaluations against state-of-the-art baselines use both open-source (Deepseek-V3.2) and closed-source (GPT-4.1) LLMs.

Wei Minn, Yan Naing Tun, Biniam Fesseha Demissie et al.

Dockerfiles enable the creation of portable container-based execution environments for the application code, and have become an important part of the modern software development process. As Dockerfiles are a form of Infrastructure-as-Code (IaC), they can include temporary workarounds and other suboptimal implementations, leading to the accrual of technical debt that affects their reliability, security, and maintainability in the future. Prior work characterized self-admitted technical debt (SATD) in Dockerfile comments and the surrounding file chunks. This single-file view is incomplete since source code evolution involves changes across different types of software artifacts such as production, test, build, and other configuration files. Thus, we address this gap by studying SATD events in Dockerfiles alongside the related source code. We find that approximately 27% of admission events and 40% of repayment events are coupled to non-Dockerfile artifacts, and coupling sources are subtype-specific. We also observed that coupled SATD in general are repaid significantly faster overall (p = 0.0201), while coupled SATD regarding missing functionalities persists longer than its isolated counterparts; Lastly, we conducted open and axial coding of coupled SATD events, and we observe that external dependency issues, more particularly regarding unreleased upstream packages and bug fixes, are the most common cause of admission triggers in the source code; we also observe that architectural refactoring is the most common prerequisite for the repayment of SATD in Dockerfiles. These findings indicate that both practitioners (e.g. developers and project managers) and SATD researchers should integrate the source code-side co-evolution, rather than the single-file view, as the primary unit of analysis.

Jiakun Liu, Shuo Shao, Wenyi Zhang et al.

A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed. Such a source model is motivated by the recent surge of interest in the semantic aspect of information: the intrinsic state corresponds to the semantic feature of the source, which in general is not observable but can only be inferred from the extrinsic observation. There are two distortion measures, one between the intrinsic state and its reproduction, and the other between the extrinsic observation and its reproduction. Under a given code rate, the tradeoff between these two distortion measures is characterized by the rate-distortion function, which is solved via the indirect rate-distortion theory and is termed as the semantic rate-distortion function of the source. As an application of the general model and its analysis, the case of Gaussian extrinsic observation is studied, assuming a linear relationship between the intrinsic state and the extrinsic observation, under a quadratic distortion structure. The semantic rate-distortion function is shown to be the solution of a convex programming programming problem with respect to an error covariance matrix, and a reverse water-filling type of solution is provided when the model further satisfies a diagonalizability condition.

Jiakun Liu, Haoxiang Zhang, Xin Xia et al.

On Stack Overflow, users reuse 11,926,354 external links to share the resources hosted outside the Stack Overflow website. The external links connect to the existing programming-related knowledge and extend the crowdsourced knowledge on Stack Overflow. Some of the external links, so-called as repeated external links, can be shared for multiple times. We observe that 82.5% of the link sharing activities (i.e., sharing links in any question, answer, or comment) on Stack Overflow share external resources, and 57.0% of the occurrences of the external links are sharing the repeated external links. However, it is still unclear what types of external resources are repeatedly shared. To help users manage their knowledge, we wish to investigate the characteristics of the repeated external links in knowledge sharing on Stack Overflow. In this paper, we analyze the repeated external links on Stack Overflow. We observe that external links that point to the text resources (hosted in documentation websites, tutorial websites, etc.) are repeatedly shared the most. We observe that: 1) different users repeatedly share the same knowledge in the form of repeated external links, thus increasing the maintenance effort of knowledge (e.g., update invalid links in multiple posts), 2) the same users can repeatedly share the external links for the purpose of promotion, and 3) external links can point to webpages with an overload of information that is difficult for users to retrieve relevant information. Our findings provide insights to Stack Overflow moderators and researchers. For example, we encourage Stack Overflow to centrally manage the commonly occurring knowledge in the form of repeated external links in order to better maintain the crowdsourced knowledge on Stack Overflow.

Jiakun Liu, Qiao Huang, Xin Xia et al.

To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. This is because deep learning frameworks are some of the most important software systems today due to their prevalent use in life-impacting deep learning applications. Moreover, the field of the development of different deep learning frameworks is the same, which enables us to find common behaviors on the removal of different types of technical debt across projects. By mining the file history of these frameworks, we find that design debt is introduced the most along the development process. As for the removal of technical debt, we find that requirement debt is removed the most, and design debt is removed the fastest. Most of test debt, design debt, and requirement debt are removed by the developers who introduced them. Based on the introduction and removal of different types of technical debt, we discuss the evolution of the frequencies of different types of technical debt to depict the unresolved sub-optimal trade-offs or decisions that are confronted by developers along the development process. We also discuss the removal patterns of different types of technical debt, highlight future research directions, and provide recommendations for practitioners.

Jiakun Liu, Xin Xia, David Lo et al.

Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted on the Internet are not available anymore. Based on our analysis of the Stack Overflow data that was released on Jun. 2, 2019, 14.2% of the links on Stack Overflow are broken links. The broken links on Stack Overflow can obstruct viewers from obtaining desired programming-related knowledge, and potentially damage the reputation of the Stack Overflow as viewers might regard the posts with broken links as obsolete. In this paper, we characterize the broken links on Stack Overflow. 65% of the broken links in our sampled questions are used to show examples, e.g., code examples. 70% of the broken links in our sampled answers are used to provide supporting information, e.g., explaining a certain concept and describing a step to solve a problem. Only 1.67% of the posts with broken links are highlighted as such by viewers in the posts' comments. Only 5.8% of the posts with broken links removed the broken links. Viewers cannot fully rely on the vote scores to detect broken links, as broken links are common across posts with different vote scores. The websites that host resources that can be maintained by their users are referenced by broken links the most on Stack Overflow -- a prominent example of such websites is GitHub. The posts and comments related to the web technologies, i.e., JavaScript, HTML, CSS, and jQuery, are associated with more broken links. Based on our findings, we shed lights for future directions and provide recommendations for practitioners and researchers.