TY  - JOUR
AV  - public
VL  - 12
JF  - Applied Sciences
A1  - Pandit, Mahesha
A1  - Gupta, Deepali
A1  - Anand, Divya
A1  - Goyal, Nitin
A1  - Aljahdali, Hani Moaiteq
A1  - Ortega-Mansilla, Arturo
A1  - Kadry, Seifedine
A1  - Kumar, Arun
TI  - Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework
N2  - 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
ID  - uninimx651
UR  - http://doi.org/10.3390/app12010493
SN  - 2076-3417
IS  - 1
Y1  - 2022/01//
KW  - software defect prediction; cross-project defect prediction; DePaaS; defect prediction as a service; cloud-based defect prediction; software defect prediction service
ER  -