APSEC 2025
Tue 2 - Fri 5 December 2025 Macao, China

The APSEC 2025 technical research track invites high-quality contributions describing original results in the discipline of software engineering. Solicited topics include, but are not limited to:

  • Tools and processes
    ○ Agile processes
    ○ DevOps and Container
    ○ Configuration Management and Deployment
    ○ Software Engineering Process and Standards

  • Requirements and Design
    ○ Service-oriented Computing
    ○ Component-based Software Engineering
    ○ Cooperative, Distributed, and Global Software Engineering
    ○ Software Architecture, Modeling and Design
    ○ Middleware, Frameworks, and APIs
    ○ Software Product-line Engineering

  • Testing and Analysis
    ○ Testing, Verification, and Validation
    ○ Program Analysis
    ○ Program Synthesis
    ○ Program Repairs

  • Formal Aspects of Software Engineering
    ○ Formal Methods
    ○ Model-driven and Domain-specific Engineering

  • Human Factors and Social Aspects of Software Engineering
    ○ Software Comprehension, Visualization, and Traceability
    ○ Software for Green and Sustainable Technologies

  • AI and Software Engineering
    ○ AI for Software Engineering ○ Software Engineering for AI ○ Search-based Software Engineering

  • Dependability, Safety, and Reliability
    ○ Reliability, availability, and safety ○ Performance ○ Vulnerability detection to enhance software security

  • Software Maintenance and Evolution
    ○ Refactoring
    ○ Reverse Engineering
    ○ Software Reuse
    ○ Software Project Management ○ Debugging, Defect Prediction, and Fault Localization

  • Software Repository Mining and Data Analytics

APSEC 2025 welcomes submissions addressing topics in a variety of application domains, including mobile, cloud, blockchains, embedded, and cyber-physical systems.

Accepted Papers

Title
A Comparative Study Towards Designing a Hybrid Architecture of Microservices and LLM-based Multi-Agent Systems
Technical Track
A First Look at Privacy Risks of Android Task-executable Voice Assistant Applications
Technical Track
AgentTCP: A Collaborative Multi-Agent Framework for Change-Aware Test Case Prioritization
Technical Track
A Hierarchical Hybrid-Intelligence Architecture with Consensus, Debate, and Reflection for High-Fidelity NL-to-PPTL Conversion
Technical Track
A Multi-Model Hybrid Framework Leveraging Query Expansion without Fine-Tuning for Zero-Shot Cross-Domain Code Search
Technical Track
An empirical comparison of data transformation techniques for clustering-based unsupervised software defect prediction
Technical Track
An Empirical Study of Reinforcement Learning-based Class Integration Test Order Generation
Technical Track
A Packing-Insensitive Detection Method for Android Malware Based on Image Representation
Technical Track
A Soft Prompt-Enhanced Device Knowledge Extraction Method for Embedded System Requirements Elicitation
Technical Track
AutoRCA: A Graph Sequence-based Automatic Root Cause Analysis Method for Microservice Systems Through Multimodal Data
Technical Track
BDafny: A Formal Execution and Verification Framework of the BPMN 2.0 in Dafny
Technical Track
BEGA:A Binary Code Embedding Method Based on Gravity-Model Augmentation Graph Contrastive Learning
Technical Track
Breaking the Curse of High-Dimensionality: A Transformer-Based Approach to Software Configuration Performance Prediction
Technical Track
CAMUS: Context-Aware Neural Mutation Selection
Technical Track
caSPESC2Vyper: Conformant and Automatic Generation from DeFi SPESC Legal Contract to Vyper Smart Contract
Technical Track
Chain of Command: Context-Aware Android Malware Detection via Permission-to-API Call Chains and Graph Representation Learning
Technical Track
Chart2Code-MoLA: Efficient Multi-Modal Code Generation via Adaptive Expert Routing
Technical Track
ChatNRC: A Non-functional Requirement Classification Framework Based on a Generative and Discriminative Mechanism
Technical Track
Cobweb: Enhanced Generation Diversity for Black-box Fairness Testing
Technical Track
Conanj: Confidential Data Analysis for Confidential Computing of Java Programs
Technical Track
Dialogue Framework for Bug Issue Types Classification in Deep Learning-oriented Projects Based on Large Language Model
Technical Track
Dynamic Variance Reduction-Based Reusable Test Case Generation for Image Classification
Technical Track
Edge4RE: A Novel Edge-Cloud Collaborative Framework for Privacy-Preserving Automated Requirements Documentation
Technical Track
Empirical assessment of the perception of graphical threat model acceptability
Technical Track
Enhancing Commit Classification for Software Maintenance with Adversarial Learning
Technical Track
Enhancing Diffusion-Driven Test Input Generation using Large Language Model
Technical Track
Exploring Developer Departure in Open-Source Software Projects: Prevalence, Reason Taxonomy, and Influencing Factors
Technical Track
Exposing and Defending Membership Leakage in Vulnerability Prediction Models
Technical Track
FixConsult: Leveraging External Defect Knowledge Driven Natural Language Suggestions Generation for Software Vulnerability Repair
Technical Track
GATUNER:Genetic Algorithm Applied to Floating-Point Precision Tuning
Technical Track
Gradient-Guided Assembly Instruction Relocation for Adversarial Attacks against Binary Code Similarity Detection
Technical Track
HGTRTracer: Advancing Requirements-Code Traceability with LLM-Augmented Attribution Reasoning and Heterogeneous Graph Transformer
Technical Track
How Do Developers Use ChatGPT for Software Test Generation? Usage Patterns, Satisfaction, and Code Adoption Analysis
Technical Track
Human Ants are Beneficial for Team Performance
Technical Track
IPSO: An Improved Particle Swarm Optimization-Based Method for Test Case Generation
Technical Track
ISA: Test Case Generation based on Improved Simulated Annealing Algorithm
Technical Track
Latent Search-Based Boundary Aware Fairness Testing for Deployed Deep Models
Technical Track
Lightweight Attention-based Temporal Modeling of Key Facial Features for Driver Fatigue Detection in Intelligent Driver Monitoring Systems
Technical Track
LLM-Based Semantic Modeling and Cooperative Evolutionary Fuzzing for Traffic Violation Scenario Generation
Technical Track
LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
Technical Track
MAAP: A Self-Evolving Multi-Agent Automated Vulnerability Repair Framework for Python
Technical Track
MicroRacer: Detecting Concurrency Bugs for Cloud Service Systems
Technical Track
Mock Clones in the Wild: An Empirical Investigation Across Six Open-Source Projects
Technical Track
MSPM: A Multi-Strategy Prompting Method for Goal-Oriented Modeling from User Stories
Technical Track
MTL-CR: A Multitask Learning Approach for Code Representation
Technical Track
MUATC: Multi-Agent Utilization to Augment Test Coverage
Technical Track
Multi-agent Assisted Automatic Test Generation for Java JSON Libraries
Technical Track
Multi-Stage Generation of Rust Unit Tests with LLMs
Technical Track
Optimizing Type Duplication in WebAssembly Module Splitting
Technical Track
PerProb: Indirectly Evaluating Memorization in Large Language Models
Technical Track
Practical Lossless Recompression of JPEG Images Using Transform Domain Prediction
Technical Track
PyReach: A Multi-Agent Framework for Vulnerability Reachability Analysis in Python
Technical Track
Recovering Variable Names in The Decompiled Code Based on Multi-Task Learning
Technical Track
Reliable Code Generation with Test Case Prioritization and Cognitive Validation
Technical Track
Seed-Adapted Cross-State Protocol Fuzzing
Technical Track
SeedOrNot: A Classification-Driven Framework for Efficient Seed Scheduling in Hybrid Fuzzing
Technical Track
Smart Contract Vulnerability Detection Based on Residual Dilated Convolution with Multi-Head Attention
Technical Track
Smart Contract Vulnerability Detection Method Based On Context-gated Perception Mechanism And Hypergraph Neural Network
Technical Track
SMTPRT:Performance Regression Testing and Localization for SMT Solvers Across Multiple Logics
Technical Track
Software Defect Prediction Based on Temporal Hypergraph Neural Network
Technical Track
SVPrompt-MR: An LLM-Based Metamorphic Relation Identification Method with Self-Verification Mechanism
Technical Track
Systematic Mapping Study on Risks and Vulnerabilities in Software Containers
Technical Track
Testing Deep Learning Libraries with Semantic Equivalent API Patterns
Technical Track
Towards Lightweight LLM Software Solutions for InsurTech: A Framework for Scalable Question Answering Systems
Technical Track
Trace: Test Repair via Agent-based Context Extraction with LLMs
Technical Track
TraceWalker: Synthesizing Interactive Debugging Progresses via Data- and Control-flow Exploration
Technical Track
Understanding Commercial Low-code App Bugs
Technical Track
Understanding Industrial Log Analysis: A Multi-Dataset Evaluation of Parsing and Anomaly Detection
Technical Track
What You See Is Not Always What You Get: Evaluating GPT's Comprehension of Source Code
Technical Track
Where Is Self-admitted Code Generated by Large Language Models on GitHub?
Technical Track

Double Blind Policy

IMPORTANT: The APSEC 2025 technical research track will use a double-blind reviewing process, which means that submissions must not reveal the authors’ identities. The authors must make every effort to honor the double-blind reviewing process. Submissions must adhere to the following rules (largely reused from APSEC 2024 double-blind instructions).

  • Omit authors’ names and institutions from your title page.
  • References to authors’ own related work must be in the third person. (For example, not “We build on our previous work…” but rather “We build on the work of…”)
  • There may be cases in which the current submission is a clear follow up of one of your previous work, and despite what recommended in the previous point, reviewers will clearly associate authorship of such a previous work to the current submission. In this case, you may decide to anonymize the reference itself at submission time. For example: “based on previous results [10]” .. where the reference is reported as “[10] Anonymous Authors. Omitted for double blind reviewing.” In doing so, however, please make sure that the APSEC 2025 submission is self-contained and its content can be reviewed and understood without accessing the previous paper.
  • Do not include acknowledgements of people, grants, organizations, etc. that would give away your identity. You may, of course, add these acknowledgements in the camera-ready version.
  • In general, aim to reduce the risk of accidental unblinding. For example, if you use an identifiable naming convention for your work, such as a project name, use a different name for your submission, which you may indicate has been changed for the purposes of double-blind reviewing. This includes names that may unblind individual authors and their institutions. For example, if your project is called GoogleDeveloperHelper, which makes it clear the work was done at Google, for the submission version, use the name DeveloperHelper or BigCompanyDeveloperHelper instead.
  • Avoid revealing the institution affiliations of authors or at which the work was performed. For example, if the evaluation includes a user study conducted with undergraduates from the CS 101 class that you teach, you might say “The study participants consist of 200 students in an introductory CS course.” You can of course add the institutional information in the camera-ready. Similar suggestions apply for work conducted in specific organizations (e.g., industrial studies). In such cases, avoid mentioning the organization’s name. Instead, you may just refer to the organization as “Org” or “Company”, etc. When appropriate and when this does not help too much in revealing the company’s name, you might mention the context (e.g., financial organization, video game development company, etc.).
  • APSEC 2025 believes in open science and that open science aids reproducibility and replicability. To improve these factors we encourage authors to consider disclosing the source code and datasets used within their paper, including extractors, survey data, etc. By sharing this information your contribution will be more impactful because others can follow up on your work and of course cite it. But please avoid linking directly to code repositories or tool deployments which can reveal your identity. Instead of doing so, please consider using services such as zenodo.org, figshare.com, which provide DOIs and support anonymous and semi-anonymous methods of archiving software and datasets. Archive.org is also recommended for the dissemination of larger datasets. These archived and anonymized datasets should be linked within the paper itself.

Call for Papers: Technical Track

It has been 32 years since the establishment of the Asia-Pacific Software Engineering Conference (APSEC) as a leading regional conference that gathers researchers and practitioners from academia, industry, and government to exchange knowledge and best practices in software engineering, and to address emerging challenges and solutions in software engineering innovation. This year’s APSEC will be held on December 2-5, 2025 in Macao, China. APSEC 2025 will continue in the tradition of previous editions of this regional conference. As in past APSEC series, the main research track features the most recent and significant innovations in the field of software engineering and all its sub-disciplines.

The APSEC 2025 technical research track invites high-quality contributions describing original results in the discipline of software engineering. Solicited topics include, but are not limited to:

  • Tools and processes
    ○ Agile processes
    ○ DevOps and Container
    ○ Configuration Management and Deployment
    ○ Software Engineering Process and Standards

  • Requirements and Design
    ○ Service-oriented Computing
    ○ Component-based Software Engineering
    ○ Cooperative, Distributed, and Global Software Engineering
    ○ Software Architecture, Modeling and Design
    ○ Middleware, Frameworks, and APIs
    ○ Software Product-line Engineering

  • Testing and Analysis
    ○ Testing, Verification, and Validation
    ○ Program Analysis
    ○ Program Synthesis
    ○ Program Repairs

  • Formal Aspects of Software Engineering
    ○ Formal Methods
    ○ Model-driven and Domain-specific Engineering

  • Human Factors and Social Aspects of Software Engineering
    ○ Software Comprehension, Visualization, and Traceability
    ○ Software for Green and Sustainable Technologies

  • AI and Software Engineering
    ○ AI for Software Engineering ○ Software Engineering for AI ○ Search-based Software Engineering

  • Dependability, Safety, and Reliability
    ○ Reliability, availability, and safety ○ Performance ○ Vulnerability detection to enhance software security

  • Software Maintenance and Evolution
    ○ Refactoring
    ○ Reverse Engineering
    ○ Software Reuse
    ○ Software Project Management ○ Debugging, Defect Prediction, and Fault Localization

  • Software Repository Mining and Data Analytics

APSEC 2025 welcomes submissions addressing topics in a variety of application domains, including mobile, cloud, blockchains, embedded, and cyber-physical systems.


Evaluation Criteria

Technical research papers must not exceed 12 pages (including references). Submissions will be evaluated by at least three program committee members. The evaluation will focus on the novelty, originality, importance to the field, proper use of research methods, and presentation of the submissions.


Submission Instructions

Submitted papers must have been neither previously accepted for publication nor concurrently submitted for review in another journal, book, conference, or workshop.

All submissions must be in English and must come in A4 paper size PDF format and conform, at the time of submission, to the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt font, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option). Also, papers must comply with the IEEE Policy on Authorship. Submissions must be submitted electronically in PDF before the due date via HotCRP.

The Chairs reserve the right to reject submissions (without reviews) that are not in compliance or out of scope for the conference.

IMPORTANT: The APSEC 2025 technical research track will use a double-blind reviewing process, which means that submissions must not reveal the authors’ identities. The authors must make every effort to honor the double-blind reviewing process. Please read the APSEC 2025 double-blind instructions carefully before preparing your paper.

Submission Link

Papers must be submitted through HotCRP: https://apsec25.hotcrp.com


Important Dates

  • Abstract Deadline (Optional): July 13, 2025 (Extended)
  • Paper Deadline: July 20, 2025 (Extended)
  • Author Notification: September 20, 2025 (Extended)
  • Camera Ready Deadline: October 20, 2025

Accepted Papers

All accepted papers will be included in the IEEE Digital Library as the APSEC 2025 conference proceedings. Accepted papers will not be permitted any additional page of content. But, we hope that the authors will reflect the reviewers’ comments as much as possible in the camera-ready version. After acceptance, the list of paper authors can not be changed under any circumstances, and the list of authors on camera-ready papers must be identical to those on submitted papers. After acceptance, paper titles can not be changed except by permission of the Program Co-Chairs, and only then when referees recommend a change for clarity or accuracy with paper content.

Conference Attendance Expectation

If a submission is accepted, at least one author of the paper must register for APSEC 2025 and present the paper at the conference. If an accepted paper is not presented, the paper will be removed from the proceedings.

Contact

  • Jacky Keung and Eunjong Choi
    APSEC 2025 Program Co-Chairs

Preprint Policy

To ensure a fair and unbiased double-blind review process, authors submitting papers to APSEC 2025 must comply with the following guidelines regarding the handling of non-anonymized versions. A non-anonymized version is considered any publicly available document (e.g., on arXiv) that contains essentially the same scientific content as the submitted paper, even if it differs in minor ways such as the title, paper structure or organization, length or formatting.

Preprints Posted Before the Anonymity Period

Authors are permitted to make a non-anonymized version of their paper publicly available before the anonymity period. The anonymity period starts one month before the submission deadline and ends when the paper is accepted, rejected, or withdrawn. However, in such cases, authors are responsible for minimizing the risk that reviewers can trivially link the submitted paper to a public version.

Specifically, authors must:

  • Take care to prevent the submitted manuscript from being associated with the non-anonymized version.
  • Not make the non-anonymized version easily discoverable via search engines by reusing distinctive phrases (e.g., title, keywords) from the submitted manuscript.
  • Not update the non-anonymized version during the anonymity period.
  • Not actively promote the non-anonymized version (e.g., via social media, blogs, or mailing lists).
  • Not reference, cite, or link to any non-anonymized version within the submitted manuscript.

Post-Publication Preprint Policy According to IEEE Guidelines

Upon acceptance and publication of a paper by IEEE, authors must update any publicly available non-anonymized version of the paper in one of the following ways:

  • Replace it with the full IEEE citation, including the Digital Object Identifier (DOI), or
  • Post the accepted version of the paper (i.e., the author’s final version prior to IEEE formatting), along with the DOI. The IEEE-published version must not be posted.

IEEE will provide each author with a preprint version of the article that includes the DOI, the IEEE copyright notice, and a statement confirming that the article has been accepted for publication by IEEE. Authors may post this preprint version in accordance with IEEE’s posting guidelines. For more details, please refer to:
Post Your Paper - IEEE Author Center Conferences