A Software Defect Prediction Model during the Test Period

2013 ◽  
Vol 475-476 ◽  
pp. 1186-1189 ◽  
Author(s):  
Wan Jiang Han ◽  
Li Xin Jiang ◽  
Xiao Yan Zhang ◽  
Yi Sun
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Project Performance ◽  
Test Period ◽  
Software Test ◽  
Defect Prediction ◽  
Software Defect ◽  
Model Defect ◽  
Multiple Defect ◽  
Defect Prediction Models

Effective defect prediction is an important topic in software engineering. This paper studies multiple defect prediction models and proposes a defect prediction model during the test period for organic project. This model is based on the analysis of project defect data and refer to Rayleigh model. Defect prediction model plays an important role in the analysis of software quality, rationally allocating resources of software test, improving the efficiency of software test. This paper selected representative software defect data to apply this model, which has been shown to improve project performance.

scholarly journals The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study

2021 ◽  
Vol 24 (68) ◽  
pp. 72-88
Author(s):  
Mohammad Alshayeb ◽  
Mashaan A. Alshammari
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Prediction Models ◽  
Fault Prediction ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Dataset Size ◽  
Defect Prediction Models ◽  
Selection Algorithms

The ongoing development of computer systems requires massive software projects. Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect prediction models are crucial for software quality assurance. This study investigates the impact of dataset size and feature selection algorithms on software defect prediction models. We use two approaches to build software defect prediction models: a statistical approach and a machine learning approach with support vector machines (SVMs). The fault prediction model was built based on four datasets of different sizes. Additionally, four feature selection algorithms were used. We found that applying the SVM defect prediction model on datasets with a reduced number of measures as features may enhance the accuracy of the fault prediction model. Also, it directs the test effort to maintain the most influential set of metrics. We also found that the running time of the SVM fault prediction model is not consistent with dataset size. Therefore, having fewer metrics does not guarantee a shorter execution time. From the experiments, we found that dataset size has a direct influence on the SVM fault prediction model. However, reduced datasets performed the same or slightly lower than the original datasets.

scholarly journals Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework

2022 ◽  
Vol 12 (1) ◽  
pp. 493
Author(s):  
Mahesha Pandit ◽  
Deepali Gupta ◽  
Divya Anand ◽  
Nitin Goyal ◽  
Hani Moaiteq Aljahdali ◽  
...  
Keyword(s):  
Software Development ◽  
Prediction Models ◽  
Easy Access ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Research And Practice ◽  
Software Defect ◽  
Software Modules ◽  
Software Development Teams ◽  
Defect Prediction Models

Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes. However, software development teams are unaware of SDP and do not have easy access to relevant models and techniques. The major reason for this problem seems to be the fragmentation of SDP research and SDP practice. To unify SDP research and practice this article introduces a cloud-based, global, unified AI framework for SDP called DePaaS—Defects Prediction as a Service. The article describes the usage context, use cases and detailed architecture of DePaaS and presents the first response of the industry practitioners to DePaaS. In a first of its kind survey, the article captures practitioner’s belief into SDP and ability of DePaaS to solve some of the known challenges of the field of software defect prediction. This article also provides a novel process for SDP, detailed description of the structure and behaviour of DePaaS architecture components, six best SDP models offered by DePaaS, a description of algorithms that recommend SDP models, feature sets and tunable parameters, and a rich set of challenges to build, use and sustain DePaaS. With the contributions of this article, SDP research and practice could be unified enabling building and using more pragmatic defect prediction models leading to increase in the efficiency of software testing.

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