Abstract
Technical debt refers to the technical trade-offs made by the software teams at the time of development of the software to fasten the delivery process. These trade-offs lead to a higher system maintenance cost, and it is difficult to often enhance the application. In some cases, enhancements can result in the entire modules being rewritten. So, in order to reduce the debt from the system, it is required to identify the debt and the project cycle where most of the debts occur. To automatically address this problem, this work uses the machine learning and text analytics model. Random forest and support vector machine (SVM) algorithms extract features from incident tracker documents and effectively classify the technical debts. The proposed technical debt classification model is made sustainable to handle the growing volumes of project documents by executing a distributed framework. The performance of both the models is examined, and it is found that SVM outperforms the other.
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Acknowledgments
This publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya Ph.D. Scheme (Unique Awardee Number: VISPHD-MEITY- 2959) of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation (Formerly Media Lab Asia).
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Rajalakshmi, V., Sendhilkumar, S., Mahalakshmi, G.S. (2021). Classification of Technical Debts in Software Development Using Text Analytics. In: Raj, J.S. (eds) International Conference on Mobile Computing and Sustainable Informatics . ICMCSI 2020. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-49795-8_31
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DOI: https://doi.org/10.1007/978-3-030-49795-8_31
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