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Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary
Chetna Gupta1, Varun Gupta2,3
1Jaypee Institute of Information Technology, Noida, India.
Peerj. Computer Science
|July 10, 2024
Summary
This study introduces a novel algorithm to automate bug management by assessing bug reports and developer capabilities. The approach significantly improves bug triage accuracy and developer workload management.
Area of Science:
- Software Engineering
- Artificial Intelligence
- Data Mining
Background:
- Bug tracking systems (BTS) are crucial for software development but often suffer from subjective and noisy bug reporting.
- Traditional bug management relies on intuition, leading to inefficiencies in prioritizing and assigning bugs.
- Lack of formal frameworks for bug attributes like severity and priority complicates data-driven decision-making.
Purpose of the Study:
- To propose a hybrid, multi-criteria fuzzy-based, and multi-objective evolutionary algorithm for automated bug management.
- To address trade-offs in multi-criteria decision-making for bug reports and developer workload.
- To enhance bug triage accuracy, differentiate developer activity, and assess developer availability.
Main Methods:
- Developed a hybrid approach combining fuzzy logic and multi-objective evolutionary algorithms.
- Created metrics for developer capability scores based on expertise, performance, and availability.
- Established metrics for relative bug importance scores.
- Gathered explicit knowledge on bug reports, developer workload, and bug priority.
Main Results:
- Achieved approximately 20% improvement over existing methods in experiments on five open-source projects.
- Obtained a harmonic mean of precision (92.05%), recall (89.04%), f-measure (90.05%), and accuracy (91.25%).
- Demonstrated effective maximization of bug throughput at the lowest cost under varying developer and bug numbers.
Conclusions:
- The proposed automated bug management approach significantly enhances triage accuracy.
- The system effectively differentiates between active and inactive developers.
- Developer availability is accurately identified based on current workload, optimizing resource allocation.
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