_Applied_  
_sciences_  
_Morally Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework_  
**[PERSON] 1, [PERSON] 1\({}^{\copyright}\)**, [PERSON] 2,3\({}^{\copyright}\)**, [PERSON] 1,*, [PERSON] 4, [PERSON] 3,5\({}^{\copyright}\)**, [PERSON] 6\({}^{\copyright}\) and [PERSON] 7  
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1 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (D.G.) \\
2 Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India; [EMAIL_ADDRESS] \\
3 Higher Polytechnic School, Universidad Europea del Atlantico, C/[PERSON] 21, 39011 Santander, Spain; [EMAIL_ADDRESS] \\
4 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 37848, Saudi Arabia; [EMAIL_ADDRESS] \\
5 Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico \\
6 Faculty of Applied Computing and Technology, Noroff University College, 94612 Kristiansand, Norway; [EMAIL_ADDRESS] \\
7 Panipat Institute of Engineering and Technology, Samalkha 132102, India; [EMAIL_ADDRESS] \\
*Correspondence: [EMAIL_ADDRESS] \\ \end{tabular}  
**Featured Application: DePaaS has the potential to be used as a global, shared platform for availing software defects prediction services by choosing appropriate base project, defect prediction model and prediction granularity. Over time, DePaaS can potentially become a rich source of defects metadata and provide deep insights into developing efficient software defects prediction models. It can promote inter-agency collaboration, data sharing, continuous improvement, and further research into application of artificial intelligence, genetic programming, and other techniques for solving key problems of software engineering.**  
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.  
**Keywords:** software defect prediction; cross-project defect prediction; DePaaS; defect prediction as a service; cloud-based defect prediction; software defect prediction service