SANER 2025
Tue 4 - Fri 7 March 2025 Montréal, Québec, Canada
Wed 5 Mar 2025 11:30 - 11:45 at L-1720 - Software Maintenance and Evolution Chair(s): Ronnie de Souza Santos

Linking software features to code components is commonly performed during software development and maintenance, including to implement a feature, document code, design test cases, trace requirements, track changes, and support inspection of safety-critical software by government and other third parties. However, manually mapping features to code is error-prone and time consuming, even for developers familiar with a system. To overcome these challenges several studies proposed automated techniques to reduce human intervention when linking features to code components. Nonetheless, three challenges remain: (i) accuracy, (ii) cost, and (iii) explainability. Linking of irrelevant code snippets causes an extra burden of analyses. If the approach lacks explainability, then a tool is less useful for many crucial systems such as safety-critical software. Moreover, heavyweight techniques such as those that require generating execution traces of every scenario or require training deep-learning models are costly and limit small companies from integrating them into their development process. We propose a contextual thematic approach that extracts the most relevant theme properties of the feature/requirement to address the aforementioned challenges. Our experiments with two proprietary projects reveal significant enhancement of performance (precision and F1 scores are more than 50% in ideal cases) in linking features to three abstractions of code components, i.e., modules, classes, and methods. Our approach is also capable of linking commits to issues in a promising way. Contextual theme extraction enhances the subjective explainability which has not yet been solved with existing approaches. Moreover, we extract several critical characteristics of the feature documents and code structures that are important to consider in both manual and automated techniques. Finally, we present the FSECAM tool for linking features to code components, which can be immediately deployed within the development process and used without much effort and cost in linking code components and commits. The replication package of our study is available online with special editor’s note https://doi.org/10.6084/m9.figshare.26426590.

Wed 5 Mar

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
Software Maintenance and EvolutionJournal First Track / Industrial Track / Research Papers at L-1720
Chair(s): Ronnie de Souza Santos University of Calgary
11:00
15m
Talk
How Effective are Large Language Models in Generating Software Specifications?
Research Papers
Danning Xie Purdue University, Byoungwoo Yoo UNIST, Nan Jiang Purdue University, Mijung Kim UNIST, Lin Tan Purdue University, Xiangyu Zhang Purdue University, Judy Lee ADP
11:15
15m
Talk
Why Do Developers Engage with ChatGPT in Issue-Tracker? Investigating Usage and Reliance on ChatGPT-Generated CodeBest Paper Award
Research Papers
Joy Krishan Das University of Saskatchewan, Saikat Mondal University of Saskatchewan, Chanchal K. Roy University of Saskatchewan, Canada
Pre-print
11:30
15m
Talk
FSECAM: A Contextual Thematic Approach for Linking Feature to Multi-level Software Architectural Components
Journal First Track
Amit Mondal Associate Professor, Khulna University, Muhammad Mainul Hossain University of Saskatchewan, Chanchal K. Roy University of Saskatchewan, Canada, Banani Roy University of Saskatchewan, Kevin Schneider University of Saskatchewan
11:45
15m
Talk
Evaluating ReLink for Traceability Link Recovery in Practice
Industrial Track
12:00
15m
Talk
Development of Automated Software Design Document Review Methods Using Large Language Models
Industrial Track
12:15
15m
Talk
Experiences on Using Large Language Models to Re-engineer a Legacy System at Volvo Group
Industrial Track
Vanshika Singh North Carolina State University, Caglar Korlu , Onur Orcun , Wesley Assunção North Carolina State University
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