Quality of Open Source Systems from Product Metrics Perspective

Software engineering and information systems practices seek ultimately to create the flawless product. One of the tools used to improve the quality of software development is the use of metrics. In this paper, metrics retrieved from open source software were analyzed for quality attributes. Defect density is considered a strong indication of the quality of software product. Few studies have taken into consideration the density of defects while looking into quality of software and proneness to defects. Analysis of this study has shown that defect density is relevant to different developers and different product sizes. Thus, open source project has shown to have low defect density and the larger the product the lower the defect density is. In addition, this study has shown that there are different metrics that correlate with each other indicating that some of these metrics have conceptual and practical relevance to each other. Another relationship was tested between the number of bugs and the metrics. Results indicated that most attributes had positive correlation with the number of bugs with exception to coupling between cohesion among methods of class.

[1]  Yang-Ming Zhu,et al.  Defect-Density Assessment in Evolutionary Product Development: A Case Study in Medical Imaging , 2013, IEEE Software.

[2]  Kenneth Magel,et al.  Empirical Evaluation of a New Coupling Metric: Combining Structural and Semantic Coupling , 2014 .

[3]  Roger S. Pressman,et al.  Software Engineering: A Practitioner's Approach , 1982 .

[4]  Geoffrey Phipps Comparing Observed Bug and Productivity Rates for Java and C++ , 1999, Softw. Pract. Exp..

[5]  Lech Madeyski,et al.  Towards identifying software project clusters with regard to defect prediction , 2010, PROMISE '10.

[6]  Saudi Arabia,et al.  Fault-Proneness of Open Source Systems: An Empirical Analysis , 2014 .

[7]  H. Abdi The Kendall Rank Correlation Coefficient , 2007 .

[8]  Deepak Khazanchi,et al.  A Study on Defect Density of Open Source Software , 2010, 2010 IEEE/ACIS 9th International Conference on Computer and Information Science.

[9]  Geoffrey Phipps Comparing observed bug and productivity rates for Java and C++ , 1999 .

[10]  Marco Torchiano,et al.  Software defect density variants: A proposal , 2013, 2013 4th International Workshop on Emerging Trends in Software Metrics (WETSoM).

[11]  Maurizio Morisio,et al.  Complexity Metrics Significance for Defects: An Empirical View , 2013, ICIT 2013.

[12]  Fumio Akiyama,et al.  An Example of Software System Debugging , 1971, IFIP Congress.

[13]  Reidar Conradi,et al.  An empirical study of software reuse vs. defect-density and stability , 2004, Proceedings. 26th International Conference on Software Engineering.

[14]  Marco Torchiano,et al.  An Overview of Software Defect Density: A Scoping Study , 2012, 2012 19th Asia-Pacific Software Engineering Conference.

[15]  Stephen H. Kan,et al.  Metrics and Models in Software Quality Engineering , 1994, SOEN.

[16]  Shari Lawrence Pfleeger,et al.  Software Metrics : A Rigorous and Practical Approach , 1998 .

[17]  R S Pressman,et al.  Software engineering: a practitioner's approach (2nd ed.) , 1986 .

[18]  Ashutosh Prasad,et al.  Open source versus closed source: software quality in monopoly and competitive markets , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Ahmed E. Hassan,et al.  Towards improving statistical modeling of software engineering data: think locally, act globally! , 2015, Empirical Software Engineering.

[20]  Michele Marchesi,et al.  An Empirical Study of Software Metrics for Assessing the Phases of an Agile Project , 2012, Int. J. Softw. Eng. Knowl. Eng..