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Handling class imbalance problem in software maintainability prediction: an empirical investigation

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Abstract

As the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality software. In research of software engineering predictive modeling, various software maintainability prediction (SMP) models are evolved to forecast maintainability. To develop a maintainability prediction model, software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability effort. This condition gives rise to a class imbalance problem (CIP). In this situation, the minority classes’ prediction, i.e., the classes demanding high maintainability effort, is a challenge. Therefore, in this direction, this study investigates three techniques for handling the CIP on ten open-source software to predict software maintainability. This empirical investigation supports the use of resampling with replacement technique (RR) for treating CIP and develop useful models for SMP.

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Authors and Affiliations

  1. Discipline of Software Engineering, Department of Computer Science & Engineering, Delhi Technological University, Delhi, 110042, India

    Ruchika Malhotra & Kusum Lata

  2. University School of Management and Entrepreneurship, East Delhi Campus, Delhi Technological University, Delhi, 110095, India

    Kusum Lata

Authors
  1. Ruchika Malhotra
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  2. Kusum Lata
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Correspondence to Kusum Lata.

Additional information

Ruchika Malhotra is Associate Head and Associate Professor in the Discipline of Software Engineering, Department of Computer Science and Engineering, Delhi Technological University (formerly Delhi College of Engineering), India. She has been awarded the prestigious UGC Raman Postdoctoral Fellowship by the Indian government for pursuing postdoctoral research from Department of Computer and Information Science, Indiana University-Purdue University, USA. She was an assistant professor at the University School of Information Technology, Guru Gobind Singh Indraprastha University, India. She has received IBM Faculty Award 2013. Her h-index is 30 as reported by Google Scholar. She can be contacted by email at ruchikamalhotra@dtu.ac.in.

Kusum Lata is currently working as Assistant Professor in University School of Management and Entrepreneurship, Delhi Technological University, India. She completed her master’s degree in Computer Technology and Applications in 2010 from the Delhi College of Engineering, University of Delhi, India. She has received her doctoral degree in Computer Science and Engineering from Delhi Technological University, India. Her research interests are in software quality improvement, applications of machine learning techniques in maintainability prediction, and validation of software metrics. She can be contacted by email at kusumlata@dtu.ac.in.

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Malhotra, R., Lata, K. Handling class imbalance problem in software maintainability prediction: an empirical investigation. Front. Comput. Sci. 16, 164205 (2022). https://doi.org/10.1007/s11704-021-0127-0

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