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A Systematic Literature Review of Soft Computing Techniques for Software Maintainability Prediction: State-of-the-Art, Challenges and Future Directions

arXiv 2209.10131 ↗First posted 21 Sep 2022cs.SE cs.AIcited by 0

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Abstract (arXiv)

The software is changing rapidly with the invention of advanced technologies and methodologies. The ability to rapidly and successfully upgrade software in response to changing business requirements is more vital than ever. For the long-term management of software products, measuring software maintainability is crucial. The use of soft computing techniques for software maintainability prediction has shown immense promise in software maintenance process by providing accurate prediction of software maintainability. To better understand the role of soft computing techniques for software maintainability prediction, we aim to provide a systematic literature review of soft computing techniques for software maintainability prediction. Firstly, we provide a detailed overview of software maintainability. Following this, we explore the fundamentals of software maintainability and the reasons for adopting soft computing methodologies for predicting software maintainability. Later, we examine the soft computing approaches employed in the process of software maintainability prediction. Furthermore, we discuss the difficulties and potential solutions associated with the use of soft computing techniques to predict software maintainability. Finally, we conclude the review with some promising future directions to drive further research innovations and developments in this promising area.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 100 references that are in the map. First posted on arXiv 21 Sep 2022.

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4 of 100 references.

References · 4

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2022XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directionscited by 10
  3. 2018A survey on software testabilitycited by 6
  4. 2021Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebarscited by 4

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