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Creators/Authors contains: "Lambaria, Noah"

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  1. ; ; ; ; ; ; (, IEEE)
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  2. ; (, Annals of Computer Science and Information Systems)
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  3. ; ; ; (, 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE))
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  4. ; ; ; ; ; ; ; ; ; (, ACM SIGAPP Applied Computing Review)
    Poor design choices, bad coding practices, or the need to produce software quickly can stand behind technical debt. Unfortunately, manually identifying and managing technical debt gets more difficult as the software matures. Recent research offers various techniques to automate the process of detecting and managing technical debt to address these challenges. This manuscript presents a mapping study of the many aspects of technical debt that have been discovered in this field of study. This includes looking at the various forms of technical debt, as well as detection methods, the financial implications, and mitigation strategies. The findings and outcomes of this study are applicable to a wide range of software development life-cycle decisions. 
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