Software process anti-pattern detection in project data

Picha, Petr; Brada, Přemek

doi:10.1145/3361149.3361169

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…The input refers to the projected need to establish modern project control models and IT resources that involve ML-based frameworks and information imprecision care, vagueness, or ambiguity by the main measures of success that is linked to the entire fields of knowledge. The introduction of modern learning assessment libraries and open-source development frameworks for project management opens up an area of study that is relevant to the technical convergence of IT resources [156,157].…”

Section: Recommendations For Management Software Processmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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Section: Recommendations For Management Software Processmentioning

confidence: 99%