Call for Papers: 1st International Workshop on AI for Software Modernization (AISM 2025)

[Raj Sharma]

The 1st International Workshop on AI for Software Modernization (AISM 2025)

16/20 November 2025, Seoul, South Korea

https://aism25.github.io/aism25/

To be held in conjunction with the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025)

 

Introduction

Application modernization is the process of upgrading software applications to enhance technology, accommodate evolving dependencies, adopt modern architectures and programming languages, and meet new business requirements. Research estimates suggest that modernization accounts for 80% of software maintenance costs, highlighting the urgent need for automation and AI-driven solutions to reduce manual effort, lower costs, and improve accuracy.

 

Common modernization efforts include:

·       Transforming legacy applications (e.g., Cobol to Java, C++ to Rust)

·       Upgrading programming languages and frameworks

·       Migrating from on-premises infrastructure to cloud-native architectures

·       Refactoring monolithic applications into microservices

While modernization is essential, it presents significant challenges, such as preserving application semantics, estimating transformation effort, and ensuring correctness after refactoring. This workshop focuses on the role of AI in software modernization. Submissions that advance traditional techniques for software modernization are also welcome.

 

Important Dates

• Submission deadline: August 18th, 2025
• Notification: September 26th, 2025
• Camera-ready deadline: October 5th, 2025

 

 

We invite high-quality, original research contributions, including but not limited to the following areas:

 

1. Application Understanding

·       AI-driven functionality detection and classification

·       Architecture extraction

·       Business rule extraction from legacy codebases

·       AI-powered question-answering and retrieval-based techniques for understanding application logic

·       AI-based code search and summarization

·       Defining and evaluating metrics for application understanding and summarization

2. Modernization Design and Effort Estimation

·       AI-driven insights on the potential impact of modernization changes, including downtime, compatibility issues, and risk mitigation strategies

·       Mapping and rearchitecting legacy applications (e.g., monolith to microservices)

·       AI-based recommendation systems for modernization planning and estimation of modernization complexity, cost, and effort

3. Application Transformation

·       Automated extraction, modularization, and migration of application functionality, database systems

·       AI-generated transformation and refactoring rules

·       Fine-tuning AI models for transformation-aware code generation

·       AI-driven automated UI modernization

·       Large-scale, multi-language migration frameworks

·       Agentic approach to feedback-driven transformation

·       AI-driven automated refactoring

4. Testing, Debugging, and Repair

·       Ensuring semantic preservation in automated transformations

·       AI-based testing strategies for modernized applications

·       Coverage metrics for program transformation correctness

·       Automated generation of functional test suites

·       AI-driven defect detection and iterative repair of transformed code

·       Defining and evaluating metrics for transformation quality

5. Case Studies and Applications

·       Real-world applications of AI in modernization

·       Development and adoption of AI-driven modernization frameworks and tools

·       Empirical studies and lessons learned in large-scale migration projects

 

Evaluation Criteria

• Novelty: Originality and technical contribution of the work
• Relevance: Alignment with the workshop’s themes and topics
• Technical Rigor: Soundness, correctness, and quality of the methodology
• Practical Impact: Applicability of the proposed techniques in real-world scenarios
• Clarity: Well-structured presentation, clear articulation of contributions, and readability

 

Submission Guidelines

• Paper Length: Submissions should belong to:
• • Full papers: 8 pages (including references), presenting mature research or industry experience papers.
  • Short papers: 4 pages (including references), presenting new ideas or early-stage work.
• Format: Papers must follow the research track formatting guidelines https://conf.researchr.org/track/ase-2025/ase-2025-papers#formatting-and-submission-guidelines
• Submission Portal:  https://cmt3.research.microsoft.com/aism2025
• Review Process: Single-blind peer review

 

Proceedings

• All accepted papers will be included in the ASE 2025's conference proceedings. The proceedings will be made available online and indexed in the ACM/IEEE Digital Library.

 

Organizers

• Diptikalyan Saha, dipt...@in.ibm.com, IBM Research India
• Srikanth Tamilselvam, srikanth.t...@in.ibm.com, IBM Research India
• Shivali Agarwal, shiv...@in.ibm.com, IBM Research India
• Sridhar Chimalakonda, c...@iittp.ac.in, IIT Tirupati

 

Program Committee

• Aseem Rastogi, Microsoft Research
• Chanchal Roy, University of Saskatchewan
• Eitan Farchi, IBM Research, Haifa
• Fumiko Satoh, IBM Research, Tokyo
• Mei Nagappan, University of Waterloo
• Paddy Krishnan, Oracle, Australia
• Raviv Gal, IBM Research, Haifa
• Raveendra Medicherla, TCS Research, India
• RD Naik, TCS Research, India
• Saurabh Sinha, IBM Research, USA
• Sujit Chakraborty, Indian Institute of Information Technology, Bangalore, India