An approach to predict software project success by cascading clustering and classification

Ramaswamy, Venkatram; Suma, V.; Pushphavathi, T. P.

doi:10.1049/ic.2012.0137

Cited by 5 publications

(2 citation statements)

References 5 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applications from the field of SPM were evaluated by the first category of papers [69][70][71][72][73]: behaviours are classified as working and pertinence. It indicated a multi-target learning problem in designing the model for estimating the system effort.…”

Section: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Hence, authors of paper [9] Authors of [13] [14] has proven the need to enhance software testing techniques. They have investigated the degree of testing that happens at every phase of software development.…”

Section: Literature Surveymentioning

confidence: 99%

Implication of Post Production Defects in Software Industries

Harekal¹,

Suma²

2015

IJCA

View full text Add to dashboard Cite

Development of defect free software is one of the primary concerns of any software industry. The main objective is to ship a high quality software product to their customers in order to attain total customer satisfaction. This is the only strategy through which industry can continue to sustain in the dynamic market. Hence, several defect management techniques are followed to reduce the defect leakage count during their production cycle. Nevertheless, the efforts laid by expert professionals in unearthing the maximum defects, yet there still prevails unidentified defects entering the customer's site. Unawareness to post production defects results in rework and overheads which is not an appreciable solution to the industry. The aim of this paper is therefore to investigate the implication of post production defects in software industries. This understanding facilitates the developing team to overcome the rate of defect injection during the production span such that post production defect leakage is reduced.

show abstract