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Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary

Chetna Gupta¹, Varun Gupta^2,3

¹Jaypee Institute of Information Technology, Noida, India.

Peerj. Computer Science

|July 10, 2024

Summary

This summary is machine-generated.

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.

Keywords:

Bug tracking system Evolutionary algorithms Software development

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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.