Good Enough Project Management Practices for Researcher Support Projects

Maimone, Christina

doi:10.1145/3332186.3332198

Cited by 1 publication

(3 citation statements)

References 4 publications

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“…We obtained articles that were related to SPM by searching using two phrases: "Machine Learning" and "Software project management". The searches were done on three digital libraries; (1) Web of Science (WOS), as it caters to multi-disciplinary research articles in the fields of science, arts, etc., (2) IEEE explore which provides articles that are specialized in the field of electrical and electronics engineering, and (3) ACM digital library, which has a comprehensive database that contains scientific articles regarding computing and information technology.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, after the second iteration, the articles can still be eliminated if SPM was not included or: (1) the paper's contribution does not consider any aspects of machine learning and project management. (2) The discussion on the paper is only focused on SPM and it does not discuss any other topic. In this work, articles undergo extensive ML, whereby the remaining articles are later categorized into categories that look into how to enhance the SPM.…”

Section: Methodsmentioning

confidence: 99%

“…Improving the efficiency and maintaining the sustainability of a software project are obstacles that are faced by project managers. The probability of project failure is generally due to the lack of knowledge, skills, resources, and technology during project implementation [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%