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Prioritizing tasks in software development: A systematic literature review.

Yegor Bugayenko1, Ayomide Bakare2, Arina Cheverda2

  • 1Huawei, Moscow, Russia.

Plos One
|April 6, 2023
PubMed
Summary
This summary is machine-generated.

This review synthesizes software engineering research on task prioritization, identifying bug prioritization as most common. Emerging trends include pull request and issue prioritization, with f-score, precision, recall, and accuracy as key metrics.

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Area of Science:

  • Software Engineering
  • Information Technology

Background:

  • Task prioritization is a critical yet complex area in software development.
  • Numerous studies exist, making it difficult for practitioners to identify effective methods.
  • This work addresses the need for a consolidated overview of research and practice.

Purpose of the Study:

  • To review the current state of research and practice in software engineering task prioritization.
  • To identify the most effective task prioritization tools and techniques used in the industry.
  • To provide guidance for IT practitioners, software developers, and project managers.

Main Methods:

  • A systematic literature review was conducted.
  • The review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.
  • Analysis focused on identifying prioritization strategies, emerging trends, and evaluation metrics.

Main Results:

  • Bug prioritization is the most prevalent strategy identified in the literature.
  • Recent research increasingly focuses on pull request and issue prioritization.
  • Common metrics for evaluating prioritization models include f-score, precision, recall, and accuracy.

Conclusions:

  • The field of task prioritization is dominated by bug prioritization strategies.
  • Future research is expected to grow in pull request and issue prioritization due to evolving software development tools.
  • Standardized metrics like f-score, precision, recall, and accuracy are crucial for assessing prioritization model performance.