Performance of Genetic Programming-based Software 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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A Classification of Software Defect Prediction Models

10.1109/nir52917.2021.9666110 ◽

2021 ◽

Author(s):

Sourabh Pal ◽

Alberto Sillitti

Keyword(s):

Prediction Models ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defect ◽

Defect Prediction Models

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Experience with Risk-Based Software Defect Prediction Models

Journal of Aerospace Computing Information and Communication ◽

10.2514/1.26507 ◽

2007 ◽

Vol 4 (1) ◽

pp. 619-627

Author(s):

Norman Schneidewind

Keyword(s):

Prediction Models ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defect ◽

Defect Prediction Models

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Incremental Feature Selection Method for Software Defect Prediction

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1252.0782s319 ◽

2019 ◽

Vol 8 (2S3) ◽

pp. 1345-1353 ◽

Cited By ~ 1

Keyword(s):

Feature Selection ◽

Software Metrics ◽

Prediction Models ◽

Search Algorithm ◽

Feature Selection Method ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defect ◽

Defect Prediction Models ◽

Selection Of

Software defect prediction models are essential for understanding quality attributes relevant for software organization to deliver better software reliability. This paper focuses mainly based on the selection of attributes in the perspective of software quality estimation for incremental database. A new dimensionality reduction method Wilk’s Lambda Average Threshold (WLAT) is presented for selection of optimal features which are used for classifying modules as fault prone or not. This paper uses software metrics and defect data collected from benchmark data sets. The comparative results confirm that the statistical search algorithm (WLAT) outperforms the other relevant feature selection methods for most classifiers. The main advantage of the proposed WLAT method is: The selected features can be reused when there is increase or decrease in database size, without the need of extracting features afresh. In addition, performances of the defect prediction models either remains unchanged or improved even after eliminating 85% of the software metrics.

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