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Using Clustering Approach to Enhance Prioritization of Regression Test Cases

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

Regression testing is necessary to maintain software quality, so it is expensive. The prioritization test case is a popular strategy for lowering this expense. When a change is made to an existing system, this testing is done to check for faults. It is more effective for test cases to be scheduled utilizing the test case prioritization technique to meet specified performance criteria. Many scholars have developed regression test case prioritizing algorithms; based on clustering methodologies to minimize the cost and improve testing's ability to find faults. We describe a method in this research that can be used to increase the effectiveness of various clustering techniques. Code complexity and code coverage are used in prioritization strategies that use clustering approaches to enhance the effectiveness of the prioritization. Ambiguities and uncertainties are present in the process of choosing an appropriate test case and locating incorrect functionalities.

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Author information

Authors and Affiliations

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

    Umakanta Dash & Arup Abhinna Acharya

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

    Satya Ranjan Dash

Authors
  1. Umakanta Dash
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  2. Arup Abhinna Acharya
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  3. Satya Ranjan Dash
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Corresponding author

Correspondence to Umakanta Dash .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Dash, U., Acharya, A.A., Dash, S.R. (2023). Using Clustering Approach to Enhance Prioritization of Regression Test Cases. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_7

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