Intelligent software engineering in the context of agile software development: A systematic literature review

CONTEXT: Intelligent Software Engineering (ISE) refers to the application of intelligent techniques to software engineering. We define an “intelligent technique” as a technique that explores data (from digital artifacts or domain experts) for knowledge discovery, reasoning, learning, planning, natur...

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Bibliographic Details
Published in:Information and software technology Vol. 119; p. 106241
Main Authors: Perkusich, Mirko, Chaves e Silva, Lenardo, Costa, Alexandre, Ramos, Felipe, Saraiva, Renata, Freire, Arthur, Dilorenzo, Ednaldo, Dantas, Emanuel, Santos, Danilo, Gorgônio, Kyller, Almeida, Hyggo, Perkusich, Angelo
Format: Journal Article
Language:English
Published: Elsevier B.V 01.03.2020
Subjects:
Agile software development
Artificial intelligence
Bayesian networks
Intelligent software engineering
Machine learning
Search-based software engineering
Bayesian networks
Artificial intelligence
Agile software development
Intelligent software engineering
Machine learning
Search-based software engineering
ISSN:0950-5849, 1873-6025
Online Access:Get full text
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Summary:CONTEXT: Intelligent Software Engineering (ISE) refers to the application of intelligent techniques to software engineering. We define an “intelligent technique” as a technique that explores data (from digital artifacts or domain experts) for knowledge discovery, reasoning, learning, planning, natural language processing, perception or supporting decision-making. OBJECTIVE: The purpose of this study is to synthesize and analyze the state of the art of the field of applying intelligent techniques to Agile Software Development (ASD). Furthermore, we assess its maturity and identify adoption risks. METHOD: Using a systematic literature review, we identified 104 primary studies, resulting in 93 unique studies. RESULTS: We identified that there is a positive trend in the number of studies applying intelligent techniques to ASD. Also, we determined that reasoning under uncertainty (mainly, Bayesian network), search-based solutions, and machine learning are the most popular intelligent techniques in the context of ASD. In terms of purposes, the most popular ones are effort estimation, requirements prioritization, resource allocation, requirements selection, and requirements management. Furthermore, we discovered that the primary goal of applying intelligent techniques is to support decision making. As a consequence, the adoption risks in terms of the safety of the current solutions are low. Finally, we highlight the trend of using explainable intelligent techniques. CONCLUSION: Overall, although the topic area is up-and-coming, for many areas of application, it is still in its infancy. So, this means that there is a need for more empirical studies, and there are a plethora of new opportunities for researchers.
ISSN:0950-5849
1873-6025
DOI:10.1016/j.infsof.2019.106241