Cost Estimation for Independent Systems Verification and Validation

Pataricza, András; Gönczy, László; Brancati, Francesco; Moreira, Francisco; Silva, Nuno; Esposito, Roberto; Bondavalli, Andrea; Esper, Alexandre

doi:10.1201/9781003337485-6

Cited by 1 publication

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“…The testing process is very crucial in the Software Development Life Cycle (SDLC), however, it consumes a significant amount of resources (roughly 40% of the overall development project costs) [1] consequently much research focused on improving the testing process to achieve the best resource https://ijicis.journals.ekb.eg/ utilization. Machine learning techniques were used intensively in the field of software testing by predicting defective software components or classifying software components into defective e.g.…”

Section: Introductionmentioning

confidence: 99%

Software Defects Prediction At Method Level Using Ensemble Learning Techniques

Ibrahim

AbdelSalam

Taj-Eddin

2023

IJICIS

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Creating error-free software artifacts is essential to increase software quality and potential re-usability. However, testing software artifacts to find defects and fix them is time consuming and costly, thus predicting the most error-prone software components can optimize the testing process by focusing testing resources on those components to save time and money. Much software defect prediction research has focused on higher granularity, e.g., file and package levels, and fewer have focused on the method level. In this paper, software defect prediction will be performed on highly imbalanced method-level datasets extracted from 23 open source Java projects. Eight ensemble learning algorithms will be applied to the datasets: Ada-Boost, Bagging, Gradient boost, Random Forest, Random Under sampling Boost, Easy Ensemble, Balanced Bagging and Balanced Random Forest. The results showed that the Balanced Random Forest classifier achieved the best results regarding recall and roc_auc values.

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Section: Introductionmentioning

confidence: 99%