Effort Estimation for Embedded Software Development Projects by Combining Machine Learning with Classification

Iwata, Kazunori; Nakashima, Toyoshiro; Anan, Yoshiyuki; Ishii, Naohiro

doi:10.1109/acit-csii-bcd.2016.058

Cited by 10 publications

(2 citation statements)

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“…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

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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: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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“…The above algorithms have a certain level of estimation accuracy for data in which outliers are excluded.Theoutliersnegativelyaffecttheestimation,butcannotbedetectedbeforetheprojects have been completed. Therefore, in this paper, we propose a two-step method for reducing the estimationerrorsusinganANNandanSVM.Projectsareclassifiedaccordingtowhethertheamount of effort is an outlier, with attributes that can be measured before the projects start used for the classification.Aftertheclassificationstage,weestablisheffortestimationmodelsforeachclassusing LR,anANN,and ε -SVR.WehavestudiedthemethodsandstatesinIwata, Nakashima,Anan& Ishii(2016).Thispaperistheextendedversionofthepaper.…”

Section: Our Contributionmentioning

confidence: 99%

Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

Iwata

Nakashima

Anan

et al. 2017

International Journal of Software Innovation

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This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

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