Predicting Software Product Quality: A Systematic Mapping Study

Sofia Ouhbi, Ali Idri, José Luis Fernández-Alemán, Ambrosio Toval


Predicting software product quality (SPQ)is becoming a permanent concern during software lifecycle phases. In this paper, a systematic mapping studywas performed to summarize the existing SPQ prediction(SPQP) approaches in literature and to organizethe selected studies according to seven classificationcriteria: SPQP approaches, research types, empiricaltypes, data sets used in the empirical evaluation of thesestudies, artifacts, SQ models, and SQ characteristics.Publication channels and trends were also identified.After identifying 182 documents in ACM Digital Library,IEEE Xplore, ScienceDirect, SpringerLink, and Googlescholar, 69 papers were selected. The results showthat the main publication source of the papers identifiedwas conference. Data mining techniques are themost frequently SPQP approaches reported in literature.Solution proposal was the main research type identified.The majority of the papers selected were history-basedevaluations using existing data which were mainly obtainedfrom open source software projects and domainspecific projects. Source code was the main artifactconcerned with SPQP approaches. Well-known SQmodels were hardly mentioned and reliability is the SQcharacteristic through which SPQP was mainly achieved.SPQP-related subject seems to need more investigationfrom researchers and practitioners. Moreover, SQ modelsand standards need to be considered more in futureSPQP research.


Prediction, software product quality, systematic mapping study

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