Software Projects Success Factors Identification using Data Mining

Yousef, Ahmed H.; Gamal, Adel; Warda, A.; Mahmoud, Mona Hanafy

doi:10.1109/icces.2006.320489

Cited by 10 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, this algorithm does add some value to our proposed model. Yousef et al built an architecture that is integrated with the software repository, which is fed to the metrics evaluator to extract software metrics. Data from the bug tracking system is integrated with different data mining models to predict defective modules in different projects and to provide hints to fix it.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel modified undersampling (MUS) technique for software defect prediction

Lingden

Alsadoon

Prasad

et al. 2019

Computational Intelligence

View full text Add to dashboard Cite

Background and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real‐world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work aims to improve the class imbalance nature of data sets to increase the accuracy of defect prediction and decrease the processing time. Methodology: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time. It consists of a modified undersampling method and a correlation feature selection (CFS) method. Results: The results from ten open source project data sets showed that the proposed model improves the accuracy in terms of F1‐score to 0.52 ∼ 0.96, and hence it is proximity reached best F1‐score value in 0.96 near to 1 then it is given a perfect performance in the prediction process. Conclusion: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time using the proposed model.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A novel modified undersampling (MUS) technique for software defect prediction

Lingden

Alsadoon

Prasad

et al. 2019

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Inadequate (software) project management practices have also been cited as reasons for ERP failure [33]. In support, [8] attributes poor software project management processes as being the major cause of ERP system failure.…”

Section: Project Management (Pm)mentioning

confidence: 99%

Categorisation of Enterprise Resource Planning (ERP) Failures: An Opportunity for Formal Methods in Computing

2019

SETWM-19, ACBES-19, EEHSS-19 Nov. 18-19, 2019 Johannesburg (South Africa)

View full text Add to dashboard Cite

Enterprise Resource Planning (ERP) systems are increasingly being used worldwide to align organisations' business processes and optimise their activities for value-proposition. However, due to the complexity of ERPs, they are often characterised with system failure resulting in huge loss of investments organisations have made in the acquisition and deployment of such systems. In this paper the components of a modern ERP are identified and reasons for such failures are elicited through a comprehensive analysis of the literature, together with a case study from the researchers' work environments. ERP failures are categorised and the techniques of formal methods (FMs) are discussed. An example of the utility of formal specification using Z, is illustrated in the context of the case study. Future work in this area is indicated as the development of an ERP failures framework, followed by a framework to address the said ERP failures.

show abstract

“…Some of the important factors affecting the software project success are efficient requirements engineering process, degree of customer involvements and effectiveness of project management process etc. [1]. Additionally, market demands development of projects within estimated time, budget and limited resources.…”

Section: Introductionmentioning

confidence: 98%

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

Ramaswamy

Suma

Pushphavathi

2012

International Conference on Software Engineering and Mobile Application Modelling and Development (ICSEMA 2012)

View full text Add to dashboard Cite

Generation of successful project is the core challenge of the day. Prediction of software project success is therefore one of the vital activities of software engineering community. Data mining techniques enable one to predict the success of the company by estimating the degree of success of their projects. This paper presents an empirical study of several projects developed at various software industries in order to comprehend the effectiveness of data mining technique for efficient project management. The paper provides K-means clustering approach for grouping of projects based on project success as one of the parameters. Subsequently, different classification algorithms are trained on the result set to build the classifier model based on K-fold cross validation. The best accuracy for the given dataset is achieved in Random Forest algorithm compared to other classifiers. This mode of project management using effective data mining techniques on empirical projects ensures accurate prediction of project success rate of the company. It further reflects process maturity leading towards implementation of strategies for improved productivity and sustainability of the company in the industrial market.

show abstract

Software Projects Success Factors Identification using Data Mining

Cited by 10 publications

References 14 publications

A novel modified undersampling (MUS) technique for software defect prediction

A novel modified undersampling (MUS) technique for software defect prediction

Categorisation of Enterprise Resource Planning (ERP) Failures: An Opportunity for Formal Methods in Computing

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

Contact Info

Product

Resources

About