IDENTIFICATION OF SOFTWARE QUALITY ATTRIBUTES FROM CODE DEFECT PREDICTION: A SYSTEMATIC LITERATURE REVIEW
Author:
Rumbutis Lukas1, Slotkienė Asta1ORCID, Pliuskuvienė Birutė1ORCID
Affiliation:
1. Department of Information Systems, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, Vilnius, Lithuania
Abstract
Identifying and understanding reasons for deriving software development defects is crucial for ensuring software product quality attributes such as maintainability. This paper presents a systematic literature review and the objective is to analyze the suggestions of other authors regarding software code defect prediction using machine learning, deep learning, or other artificial intelligence methods for the identification of software quality. The systemic literature review reveals that many analyzed papers considered multiple software code defects, but they were analyzed individually. However, more is needed to identify software quality attributes. The more profound analysis of code smells indicates the significance when considering multiple detected code smells and their interconnectedness; it helps to identify the software quality sub-attributes of maintainability.
Publisher
Vilnius Gediminas Technical University
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