Artículos

 

Predicting Software Product Quality: A Systematic Mapping Study

 

Sofia Ouhbi¹, Ali Idri¹, José Luis Fernández-Alemán², Ambrosio Toval²

 

¹ University Mohammed V, Software Project Management Research Team, ENSIAS, Rabat, Morocco. ouhbisofia@gmail.com, idri@ensias.ma

² University of Murcia, Department of Informatics and Systems, Faculty of Computer Science, Murcia, Spain. aleman@um.es, atoval@um.es

Corresponding author is Sofia Ouhbi.

 

Article received on 14/04/2014.
Accepted on 10/06/2015.

 

Abstract

Predicting software product quality (SPQ) is becoming a permanent concern during software life cycle phases. In this paper, a systematic mapping study was performed to summarize the existing SPQ prediction (SPQP) approaches in literature and to organize the selected studies according to seven classification criteria: SPQP approaches, research types, empirical types, data sets used in the empirical evaluation of these studies, 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 Google scholar, 69 papers were selected. The results show that the main publication source of the papers identified was conference. Data mining techniques are the most frequently SPQP approaches reported in literature. Solution proposal was the main research type identified. The majority of the papers selected were history-based evaluations using existing data which were mainly obtained from open source software projects and domain specific projects. Source code was the main artifact concerned with SPQP approaches. Well-known SQ models were hardly mentioned and reliability is the SQ characteristic through which SPQP was mainly achieved. SPQP-related subject seems to need more investigation from researchers and practitioners. Moreover, SQ models and standards need to be considered more in future SPQP research.

Keywords: Prediction, software product quality, systematic mapping study.

 

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Acknowledgments

This research is part of the project Software Project Management using Data Mining Techniques, (AP2010-2013), financed by Mohammed V University (Morocco), and part of the project GEODAS-REQ (TIN2012-37493-C03-02) financed by both the Spanish Ministry of Economy and Competitiveness and European FEDER funds. The mobility grant of Sofia Ouhbiis financed by the Mediterranean Office for Youth (MOY).

 

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