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Predicting Code Merge Conflicts and Selecting Optimal Code Branching Strategy for Quality Improvement in Banking Sector

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Book cover System Performance and Management Analytics

Part of the book series: Asset Analytics ((ASAN))

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Abstract

Code branching and merging plays a very critical role in the software development in an enterprise. Branching provides parallel development by enabling several development teams to work in isolation on multiple piece of code in parallel without impacting each other. Merging is a process to integrate the code of different teams together, which is achieved by moving the code around the branches. The process of merging can be very troublesome as it may contribute to enormous code merge or integration defects also known as code merge conflicts. One of the major problems faced by the practitioners is to predict the number of code merge conflicts and plan for the resolution of these conflicts. Another problem that is faced in an enterprise is to select an appropriate code branching strategy. Selection of a suitable code branching strategy is a multi-criteria decision making problem which involves multiple criteria and alternatives. This paper proposes a hybrid approach for predicting code merge conflicts and selecting the most suitable code branching strategy. Artificial neural network (ANN) is applied in a large enterprise to predict the code merge conflicts; thereafter analytic hierarchy process (AHP) is applied to select the most suitable code branching strategy. Total four code branching strategies have been considered in this paper. The outcome from the proposed approach successfully predicts the number of code conflicts and selects Branching Set-A as the most suitable code branching strategy with the highest priority weight of 0.287. The proposed methodology proved out to be very useful instrument for enterprises to quantitatively predict code merge conflicts and select the most suitable code branching strategy.

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Authors and Affiliations

  1. Amity Institute of Information Technology, Amity University, Noida, 201313, Uttar Pradesh, India

    Viral Gupta & Deepak Kumar

  2. Centre for Interdisciplinary Research, Amity University, Noida, 201313, Uttar Pradesh, India

    P. K. Kapur

  3. Department of Computer Science, BIT, Mesra, India

    S. P. Singh

Authors
  1. Viral Gupta
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  2. P. K. Kapur
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  3. Deepak Kumar
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  4. S. P. Singh
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Corresponding author

Correspondence to Viral Gupta .

Editor information

Editors and Affiliations

  1. Center for Interdisciplinary Research, Amity University, New Delhi, India

    P. K. Kapur

  2. Department of Economics and Management in Machine Building, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

    Yury Klochkov

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Ajit Kumar Verma

  4. Amity University, Noida, Uttar Pradesh, India

    Gurinder Singh

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Gupta, V., Kapur, P.K., Kumar, D., Singh, S.P. (2019). Predicting Code Merge Conflicts and Selecting Optimal Code Branching Strategy for Quality Improvement in Banking Sector. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds) System Performance and Management Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_2

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