arXiv subjects

Peng Liang

Publications and source records attributed to Peng Liang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Assessing UML Diagrams by GPT: Implications for Education

In software engineering (SE) research and practice, UML is well known as an essential modeling methodology for requirements analysis and software modeling in both academia and industry. In particular, fundamental knowledge of UML modeling and practice in creating high-quality UML diagrams are included in SE-relevant courses in the undergraduate programs of many universities. This leads to a time-consuming and labor-intensive task for educators to review and grade a large number of UML diagrams created by the students. Recent advances in generative AI techniques, such as GPT, have paved new ways to automate many SE tasks. However, current research or tools seldom explore the capabilities of GPT in evaluating the quality of UML diagrams. This paper aims to investigate the feasibility and performance of GPT in assessing the quality of UML use case diagrams, class diagrams, and sequence diagrams. First, 11 evaluation criteria with grading details were proposed for these UML diagrams. Next, a series of experiments was designed and conducted on 40 students' UML modeling reports to explore the performance of GPT in evaluating and grading these UML diagrams. The research findings reveal that GPT can complete this assessment task, but it cannot replace human experts yet. Meanwhile, there are five evaluation discrepancies between GPT and human experts. These discrepancies vary in the use of different evaluation criteria in different types of UML diagrams, presenting GPT's strengths and weaknesses in this automatic evaluation task.

cs.SE↗

arXiv2025

ParaDySe: A Parallel-Strategy Switching Framework for Dynamic Sequence Lengths in Transformer

Dynamic sequences with varying lengths have been widely used in the training of Transformer-based large language models (LLMs). However, current training frameworks adopt a pre-defined static parallel strategy for these sequences, causing neither communication-parallelization cancellation on short sequences nor out-of-memory on long sequences. To mitigate these issues, we propose ParaDySe, a novel adaptive Parallel strategy switching framework for Dynamic Sequences. ParaDySe enables on-the-fly optimal strategy adoption according to the immediate input sequence. It first implements the modular function libraries for parallel strategies with unified tensor layout specifications, and then builds sequence-aware memory and time cost models with hybrid methods. Guided by cost models, ParaDySe selects optimal layer-wise strategies for dynamic sequences via an efficient heuristic algorithm. By integrating these techniques together, ParaDySe achieves seamless hot-switching of optimal strategies through its well-designed function libraries. We compare ParaDySe with baselines on representative LLMs under datasets with sequence lengths up to 624K. Experimental results indicate that ParaDySe addresses OOM and CPC bottlenecks in LLM training by systematically integrating long-sequence optimizations with existing frameworks.

cs.LG↗

arXiv2025

Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review

Public Code Review (PCR) is developed in the Software Question Answering (SQA) community, assisting developers in exploring high-quality and efficient review services. Current methods on PCR mainly focus on the reviewer's perspective, including finding a capable reviewer, predicting comment quality, and recommending/generating review comments. However, it is not well studied that how to satisfy the review necessity requests posted by developers which can increase their visibility, which in turn acts as a prerequisite for better review responses. To this end, we propose K nowledge-guided P rompt learning for P ublic Code Review (KP-PCR) to achieve developer-based code review request quality assurance (i.e., predicting request necessity and recommending tags subtask). Specifically, we reformulate the two subtasks via 1) text prompt tuning which converts both of them into a Masked Language Model (MLM) by constructing prompt templates using hard prompt; and 2) knowledge and code prefix tuning which introduces knowledge guidance from fine-tuned large language models by soft prompt, and uses program dependence graph to characterize code snippets. Finally, both of the request necessity prediction and tag recommendation subtasks output predicted results through an answer engineering module. In addition, we further analysis the time complexity of our KP-PCR that has lightweight prefix based the operation of introducing knowledge guidance. Experimental results on the PCR dataset for the period 2011-2023 demonstrate that our KP-PCR outperforms baselines by 2.3%-8.4% in the request necessity prediction and by 1.4%-6.9% in the tag recommendation. The code implementation is released at https://github.com/WUT-IDEA/KP-PCR

cs.SE↗

arXiv2025

On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices

AI code generation tools have gained significant popularity among developers, who use them to assist in software development due to their capability to generate code. Existing studies mainly explored the quality, e.g., correctness and security, of AI-generated code, while in real-world software development, the prerequisite is to distinguish AI-generated code from human-written code, which emphasizes the need to explicitly declare AI-generated code by developers. To this end, this study intends to understand the ways developers use to self-declare AI-generated code and explore the reasons why developers choose to self-declare or not. We conducted a mixed-methods study consisting of two phases. In the first phase, we mined GitHub repositories and collected 613 instances of AI-generated code snippets. In the second phase, we conducted a follow-up practitioners' survey, which received 111 valid responses. Our research revealed the practices followed by developers to self-declare AI-generated code. Most practitioners (76.6%) always or sometimes self-declare AI-generated code. In contrast, other practitioners (23.4%) noted that they never self-declare AI-generated code. The reasons for self-declaring AI-generated code include the need to track and monitor the code for future review and debugging, and ethical considerations. The reasons for not self-declaring AI-generated code include extensive modifications to AI-generated code and the developers' perception that self-declaration is an unnecessary activity. We finally provided guidelines for practitioners to self-declare AI-generated code, addressing ethical and code quality concerns.

cs.SE↗

arXiv2025

ArchISMiner: A Framework for Automatic Mining of Architectural Issue-Solution Pairs from Online Developer Communities

Stack Overflow (SO), a leading online community forum, is a rich source of software development knowledge. However, locating architectural knowledge, such as architectural solutions remains challenging due to the overwhelming volume of unstructured content and fragmented discussions. Developers must manually sift through posts to find relevant architectural insights, which is time-consuming and error-prone. This study introduces ArchISMiner, a framework for mining architectural knowledge from SO. The framework comprises two complementary components: ArchPI and ArchISPE. ArchPI trains and evaluates multiple models, including conventional ML/DL models, Pre-trained Language Models (PLMs), and Large Language Models (LLMs), and selects the best-performing model to automatically identify Architecture-Related Posts (ARPs) among programming-related discussions. ArchISPE employs an indirect supervised approach that leverages diverse features, including BERT embeddings and local TextCNN features, to extract architectural issue-solution pairs. Our evaluation shows that the best model in ArchPI achieves an F1-score of 0.960 in ARP detection, and ArchISPE outperforms baselines in both SE and NLP fields, achieving F1-scores of 0.883 for architectural issues and 0.894 for solutions. A user study further validated the quality (e.g., relevance and usefulness) of the identified ARPs and the extracted issue-solution pairs. Moreover, we applied ArchISMiner to three additional forums, releasing a dataset of over 18K architectural issue-solution pairs. Overall, ArchISMiner can help architects and developers identify ARPs and extract succinct, relevant, and useful architectural knowledge from developer communities more accurately and efficiently. The replication package of this study has been provided at https://github.com/JeanMusenga/ArchISPE

cs.SE↗

arXiv2025

Quantum Computing as a Service -- a Software Engineering Perspective

Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics via programmable quantum bits (QuBits) - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket, IBM Qiskit), and consortiums like 'Quantum Flagship' are striving to develop practically capable and commercially viable quantum computing (QC) systems and technologies. Quantum Computing as a Service (QCaaS) is viewed as a solution attuned to the philosophy of service-orientation that can offer QC resources and platforms, as utility computing, to individuals and organisations who do not own quantum computers. This research investigates a process-centric and architecture-driven approach to offer a software engineering perspective on enabling QCaaS - a.k.a quantum service-orientation. We employed a two-phase research method comprising (a) a systematic mapping study and (b) an architecture-based development, first to identify the phases of the quantum service development life cycle and subsequently to integrate these phases into a reference architecture that supports QCaaS. The SMS process retrieved a collection of potentially relevant research literature and based on a multi-step selection and qualitative assessment, we selected 41 peer-reviewed studies to answer three RQs. The RQs investigate (i) demographic details in terms of frequency, types, and trends of research, (ii) phases of quantum service development lifecycle to derive a reference architecture for conception, modeling, assembly, and deployment of services, and (iii) The results identify a 4-phased development lifecycle along with quantum significant requirements (QSRs), various modeling notations, catalogue of patterns, programming languages, and deployment platforms that can be integrated in a layered reference architecture to engineer QCaaS.

cs.SE↗

arXiv2025

Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair

LLMs have garnered considerable attention for their potential to streamline Automated Program Repair (APR). LLM-based approaches can either insert the correct code or directly generate patches when provided with buggy methods. However, most of LLM-based APR methods rely on a single type of software information, without fully leveraging different software artifacts. Despite this, many LLM-based approaches do not explore which specific types of information best assist in APR. Addressing this gap is crucial for advancing LLM-based APR techniques. We propose DEVLoRe to use issue content (description and message) and stack error traces to localize buggy methods, then rely on debug information in buggy methods and issue content and stack error to localize buggy lines and generate plausible patches which can pass all unit tests. The results show that while issue content is particularly effective in assisting LLMs with fault localization and program repair, different types of software artifacts complement each other. By incorporating different artifacts, DEVLoRe successfully locates 49.3% and 47.6% of single and non-single buggy methods and generates 56.0% and 14.5% plausible patches for the Defects4J v2.0 dataset, respectively. This outperforms current state-of-the-art APR methods. Furthermore, we re-implemented and evaluated our framework, demonstrating its effectiveness in its effectiveness in resolving 9 unique issues compared to other state-of-the-art frameworks using the same or more advanced models on SWE-bench Lite.We also discussed whether a leading framework for Python code can be directly applied to Java code, or vice versa. The source code and experimental results of this work for replication are available at https://github.com/XYZboom/DEVLoRe.

cs.SE↗

arXiv2025

Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity

In the field of software traceability link recovery (TLR), textual similarity has long been regarded as the core criterion. However, in tasks involving natural language and programming language (NL-PL) artifacts, relying solely on textual similarity is limited by their semantic gap. To this end, we conducted a large-scale empirical evaluation across various types of TLR tasks, revealing the limitations of textual similarity in NL-PL scenarios. To address these limitations, we propose an approach that incorporates multiple domain-specific auxiliary strategies, identified through empirical analysis, into two models: the Heterogeneous Graph Transformer (HGT) via edge types and the prompt-based Gemini 2.5 Pro via additional input information. We then evaluated our approach using the widely studied requirements-to-code TLR task, a representative case of NL-PL TLR. Experimental results show that both the multi-strategy HGT and Gemini 2.5 Pro models outperformed their original counterparts without strategy integration. Furthermore, compared to the current state-of-the-art method HGNNLink, the multi-strategy HGT and Gemini 2.5 Pro models achieved average F1-score improvements of 3.68% and 8.84%, respectively, across twelve open-source projects, demonstrating the effectiveness of multi-strategy integration in enhancing overall model performance for the requirements-code TLR task.

cs.SE↗

arXiv2025

A Survey on the Techniques and Tools for Automated Requirements Elicitation and Analysis of Mobile Apps

[Background:] Research on automated requirements elicitation and analysis of mobile apps employed lots of techniques and tools proposed by RE researchers and practitioners. However, little is known about the characteristics of these techniques and tools as well as the RE tasks in requirements elicitation and analysis that got supported with the help of respective techniques and tools. [Aims:] The goal of this paper is to investigate the state-of-the-art of the techniques and tools used in automated requirements elicitation and analysis of mobile apps. [Method:] We carried out a systematic mapping study by following the guidelines of Kitchenham et al. [Results:] Based on 73 selected papers, we found the most frequently used techniques - semi-automatic techniques, and the main characteristics of the tools - open-sourced and non-self-developed tools for requirements analysis and text pre-processing. Plus, the most three investigated RE tasks are requirements analysis, mining and classification. [Conclusions:] Our most important conclusions are: (1) there is a growth in the use of techniques and tools in automated requirements elicitation and analysis of mobile apps, (2) semi-automatic techniques are mainly used in the publications on this research topic, (3) requirements analysis, mining and classification are the top three RE tasks with the support of automatic techniques and tools, and (4) the most popular tools are open-sourced and non-self-developed, and they are mainly used in requirements analysis and text processing.

cs.SE↗

arXiv2025

Towards the Datasets Used in Requirements Engineering of Mobile Apps: Preliminary Findings from a Systematic Mapping Study

[Background] Research on requirements engineering (RE) for mobile apps employs datasets formed by app users, developers or vendors. However, little is known about the sources of these datasets in terms of platforms and the RE activities that were researched with the help of the respective datasets. [Aims] The goal of this paper is to investigate the state-of-the-art of the datasets of mobile apps used in existing RE research. [Method] We carried out a systematic mapping study by following the guidelines of Kitchenham et al. [Results] Based on 43 selected papers, we found that Google Play and Apple App Store provide the datasets for more than 90% of published research in RE for mobile apps. We also found that the most investigated RE activities - based on datasets, are requirements elicitation and requirements analysis. [Conclusions] Our most important conclusions are: (1) there is a growth in the use of datasets for RE research of mobile apps since 2012, (2) the RE knowledge for mobile apps might be skewed due to the overuse of Google Play and Apple App Store, (3) there are attempts to supplement reviews of apps from repositories with other data sources, (4) there is a need to expand the alternative sources and experiments with complimentary use of multiple sources, if the community wants more generalizable results. Plus, it is expected to expand the research on other RE activities, beyond elicitation and analysis.

cs.SE↗

arXiv2025

How Do Users Revise Architectural Related Questions on Stack Overflow: An Empirical Study

Technical Questions and Answers (Q&A) sites, such as Stack Overflow (SO), accumulate a significant variety of information related to software development in posts from users. To ensure the quality of this information, SO encourages its users to review posts through various mechanisms (e.g., question and answer revision processes). Although Architecture Related Posts (ARPs) communicate architectural information that has a system-wide impact on development, little is known about how SO users revise information shared in ARPs. To fill this gap, we conducted an empirical study to understand how users revise Architecture Related Questions (ARQs) on SO. We manually checked 13,205 ARPs and finally identified 4,114 ARQs that contain revision information. Our main findings are that: (1) The revision of ARQs is not prevalent in SO, and an ARQ revision starts soon after this question is posted (i.e., from 1 minute onward). Moreover, the revision of an ARQ occurs before and after this question receives its first answer/architecture solution, with most revisions beginning before the first architecture solution is posted. Both Question Creators (QCs) and non-QCs actively participate in ARQ revisions, with most revisions being made by QCs. (2) A variety of information (14 categories) is missing and further provided in ARQs after being posted, among which design context, component dependency, and architecture concern are dominant information. (3) Clarify the understanding of architecture under design and improve the readability of architecture problem are the two major purposes of the further provided information in ARQs. (4) The further provided information in ARQs has several impacts on the quality of answers/architecture solutions, including making architecture solution useful, making architecture solution informative, making architecture solution relevant, among others.

cs.SE↗

arXiv2025

MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration

Software architecture design is a critical, yet inherently complex and knowledge-intensive phase of software development. It requires deep domain expertise, development experience, architectural knowledge, careful trade-offs among competing quality attributes, and the ability to adapt to evolving requirements. Traditionally, this process is time-consuming and labor-intensive, and relies heavily on architects, often resulting in limited design alternatives, especially under the pressures of agile development. While Large Language Model (LLM)-based agents have shown promising performance across various SE tasks, their application to architecture design remains relatively scarce and requires more exploration, particularly in light of diverse domain knowledge and complex decision-making. To address the challenges, we proposed MAAD (Multi-Agent Architecture Design), an automated framework that employs a knowledge-driven Multi-Agent System (MAS) for architecture design. MAAD orchestrates four specialized agents (i.e., Analyst, Modeler, Designer and Evaluator) to collaboratively interpret requirements specifications and produce architectural blueprints enriched with quality attributes-based evaluation reports. We then evaluated MAAD through a case study and comparative experiments against MetaGPT, a state-of-the-art MAS baseline. Our results show that MAAD's superiority lies in generating comprehensive architectural components and delivering insightful and structured architecture evaluation reports. Feedback from industrial architects across 11 requirements specifications further reinforces MAAD's practical usability. We finally explored the performance of the MAAD framework with three LLMs (GPT-4o, DeepSeek-R1, and Llama 3.3) and found that GPT-4o exhibits better performance in producing architecture design, emphasizing the importance of LLM selection in MAS-driven architecture design.

cs.SE↗

arXiv2025

Contextual Code Retrieval for Commit Message Generation: A Preliminary Study

A commit message describes the main code changes in a commit and plays a crucial role in software maintenance. Existing commit message generation (CMG) approaches typically frame it as a direct mapping which inputs a code diff and produces a brief descriptive sentence as output. However, we argue that relying solely on the code diff is insufficient, as raw code diff fails to capture the full context needed for generating high-quality and informative commit messages. In this paper, we propose a contextual code retrieval-based method called C3Gen to enhance CMG by retrieving commit-relevant code snippets from the repository and incorporating them into the model input to provide richer contextual information at the repository scope. In the experiments, we evaluated the effectiveness of C3Gen across various models using four objective and three subjective metrics. Meanwhile, we design and conduct a human evaluation to investigate how C3Gen-generated commit messages are perceived by human developers. The results show that by incorporating contextual code into the input, C3Gen enables models to effectively leverage additional information to generate more comprehensive and informative commit messages with greater practical value in real-world development scenarios. Further analysis underscores concerns about the reliability of similaritybased metrics and provides empirical insights for CMG.

cs.SE↗

arXiv2025

iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development

Requirements development is a critical phase as it is responsible for providing a clear understanding of what stakeholders need. It involves collaboration among stakeholders to extract explicit requirements and address potential conflicts, which is time-consuming and labor-intensive. Recently, multi-agent systems for software development have attracted much attention. However, existing research provides limited support for requirements development and overlooks the injection of human knowledge into agents and the human-agent collaboration. % To address these issues, this paper proposes a knowledge-driven multi-agent framework for intelligent requirement development, named iReDev. iReDev features: iReDev consists of six knowledge-driven agents to support the entire requirements development. They collaboratively perform various tasks to produce a software requirements specification. iReDev focuses on integrating human knowledge for agents, enabling them to simulate real-world stakeholders. iReDev uses an event-driven communication mechanism based on an artifact pool. Agents continuously monitor the pool and autonomously trigger the next action based on its changes, enabling iReDev to handle new requirements quickly. iReDev introduces a human-in-the-loop mechanism to support human-agent collaboration, ensuring that the generated artifacts align with the expectations of stakeholders. We evaluated the generated artifacts and results show that iReDev outperforms existing baselines in multiple aspects. We further envision three key directions and hope this work can facilitate the development of intelligent requirements development.

cs.SE↗

arXiv2025

Opt-GPTQ: An Optimized GPTQ Combining Sparse Attention and Quantization Techniques

In the field of deep learning, traditional attention mechanisms face significant challenges related to high computational complexity and large memory consumption when processing long sequence data. To address these limitations, we propose Opt-GPTQ, an optimized Gradient-based Post Training Quantization (GPTQ) combining the Grouped Query Attention (GQA) mechanism with paging memory management, optimizing the traditional Multi-Head Attention (MHA) mechanism by grouping query heads and sharing key-value vectors. Optimized GQA (Opt-GQA) effectively reduces computational complexity, minimizes memory fragmentation, and enhances memory utilization for large-scale models. Opt-GPTQ is optimized for Data Center Units (DCUs) and integrated into the vLLM model to maximize hardware efficiency. It customizes GPU kernels to further enhance attention computation by reducing memory access latency and boosting parallel computing capabilities. Opt-GQA integrates Attention with Linear Biases (ALiBi) to reduce overhead and enhance long-sequence processing. Experimental results show that Opt-GPTQ significantly reduces computation time and memory usage while improving model performance.

cs.DC↗

arXiv2025

Finding Compiler Bugs through Cross-Language Code Generator and Differential Testing

Compilers play a central role in translating high-level code into executable programs, making their correctness essential for ensuring code safety and reliability. While extensive research has focused on verifying the correctness of compilers for single-language compilation, the correctness of cross-language compilation - which involves the interaction between two languages and their respective compilers - remains largely unexplored. To fill this research gap, we propose CrossLangFuzzer, a novel framework that introduces a universal intermediate representation (IR) for JVM-based languages and automatically generates cross-language test programs with diverse type parameters and complex inheritance structures. After generating the initial IR, CrossLangFuzzer applies three mutation techniques - LangShuffler, FunctionRemoval, and TypeChanger - to enhance program diversity. By evaluating both the original and mutated programs across multiple compiler versions, CrossLangFuzzer successfully uncovered 10 confirmed bugs in the Kotlin compiler, 4 confirmed bugs in the Groovy compiler, 7 confirmed bugs in the Scala 3 compiler, 2 confirmed bugs in the Scala 2 compiler, and 1 confirmed bug in the Java compiler. Among all mutators, TypeChanger is the most effective, detecting 11 of the 24 compiler bugs. Furthermore, we analyze the symptoms and root causes of cross-compilation bugs, examining the respective responsibilities of language compilers when incorrect behavior occurs during cross-language compilation. To the best of our knowledge, this is the firstwork specifically focused on identifying and diagnosing compiler bugs in cross-language compilation scenarios. Our research helps to understand these challenges and contributes to improving compiler correctness in multi-language environments.

cs.PL↗

arXiv2025

Containerization in Multi-Cloud Environment: Roles, Strategies, Challenges, and Solutions for Effective Implementation

Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of containerization in multi-cloud environment. We conducted the Systematic Mapping Study (SMS) on the literature published between January 2013 and July 2024. One hundred twenty one studies were selected and the key results are: (1) Four leading themes on containerization in multi-cloud environment are identified: 'Scalability and High Availability', 'Performance and Optimization', 'Security and Privacy', and 'Multi-Cloud Container Monitoring and Adaptation'. (2) Ninety-eight patterns and strategies for containerization in multicloud environment were classified across 10 subcategories and 4 categories. (3) Ten quality attributes considered were identified with 47 associated tactics. (4) Four catalogs consisting of challenges and solutions related to security, automation, deployment, and monitoring were introduced. The results of this SMS will assist researchers and practitioners in pursuing further studies on containerization in multi-cloud environment and developing specialized solutions for containerization applications in multi-cloud environment.

cs.DC↗

arXiv2025

How Do OSS Developers Reuse Architectural Solutions from Q&A Sites: An Empirical Study

Developers reuse programming-related knowledge on Q&A sites that functionally matches the programming problems they encounter in their development. Despite extensive research on Q&A sites, being a high-level and important type of development-related knowledge, architectural solutions and their reuse are rarely explored. To fill this gap, we conducted a mixed-methods study that includes a mining study and a survey study. For the mining study, we mined 984 commits and issues from 893 OSS projects on GitHub that explicitly referenced architectural solutions from SO and SWESE. For the survey study, we identified practitioners involved in the reuse of these architectural solutions and surveyed 227 of them to further understand how practitioners reuse architectural solutions from Q&A sites in their OSS development. Our findings: (1) OSS practitioners use architectural solutions from Q&A sites to solve a large variety of architectural problems, wherein Component design issue, Architectural anti-pattern, and Security issue are dominant; (2) Seven categories of architectural solutions from Q&A sites have been reused to solve those problems, among which Architectural refactoring, Use of frameworks, and Architectural tactic are the three most reused architectural solutions; (3) OSS developers often rely on ad hoc ways (e.g., informal, improvised, or unstructured approaches) to incorporate architectural solutions from SO, drawing on personal experience and intuition rather than standardized or systematic practices; (4) Using architectural solutions from SO comes with a variety of challenges, e.g., OSS practitioners complain that they need to spend significant time to adapt such architectural solutions to address design concerns raised in their OSS development, and it is challenging to use architectural solutions that are not tailored to the design context of their OSS projects.

cs.SE↗