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A Systematic Literature Review of Machine Learning Estimation Approaches in Scrum Projects

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Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1040))

Abstract

It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx. 43% of the projects are often delivered late and entered crises because of overbudget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g., Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon for strengthening the collaboration between developer and customer. Estimation has always been challenging in Agile as requirements are volatile. This encourages researchers to work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paper reviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation.

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Authors and Affiliations

  1. Lovely Professional University, Phagwara, Punjab, India

    Mohit Arora, Sahil Verma,  Kavita & Shivali Chopra

Authors
  1. Mohit Arora
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  2. Sahil Verma
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  3. Kavita
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  4. Shivali Chopra
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Corresponding author

Correspondence to Sahil Verma .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, India

    Akash Kumar Bhoi

  4. Division of Information and Communication, Baekseok University, Cheonan-si, Ch’ungch’ong-namdo, Korea (Republic of)

    Gyoo-Soo Chae

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Arora, M., Verma, S., Kavita, Chopra, S. (2020). A Systematic Literature Review of Machine Learning Estimation Approaches in Scrum Projects. In: Mallick, P., Balas, V., Bhoi, A., Chae, GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-15-1451-7_59

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