eprintid: 651 rev_number: 12 eprint_status: archive userid: 2 dir: disk0/00/00/06/51 datestamp: 2022-05-06 23:56:12 lastmod: 2023-07-18 23:30:11 status_changed: 2022-05-06 23:56:12 type: article metadata_visibility: show creators_name: Pandit, Mahesha creators_name: Gupta, Deepali creators_name: Anand, Divya creators_name: Goyal, Nitin creators_name: Aljahdali, Hani Moaiteq creators_name: Ortega-Mansilla, Arturo creators_name: Kadry, Seifedine creators_name: Kumar, Arun creators_id: creators_id: creators_id: divya.anand@uneatlantico.es creators_id: creators_id: creators_id: arturo.ortega@uneatlantico.es creators_id: creators_id: title: Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: public keywords: software defect prediction; cross-project defect prediction; DePaaS; defect prediction as a service; cloud-based defect prediction; software defect prediction service abstract: 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 date: 2022-01 publication: Applied Sciences volume: 12 number: 1 pagerange: 493 id_number: doi:10.3390/app12010493 refereed: TRUE issn: 2076-3417 official_url: http://doi.org/10.3390/app12010493 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés 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 metadata Pandit, Mahesha; Gupta, Deepali; Anand, Divya; Goyal, Nitin; Aljahdali, Hani Moaiteq; Ortega-Mansilla, Arturo; Kadry, Seifedine y Kumar, Arun mail SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework. Applied Sciences, 12 (1). p. 493. ISSN 2076-3417 document_url: http://repositorio.uneatlantico.es/id/eprint/651/1/applsci-12-00493-v3.pdf