Requirements attributes to predict requirements related defects

Literature suggests that requirements defects are a very costly problem to fix. Understanding how requirements changes influence the overall quality of software is important. Having some defect predictors at the requirements stage may help the stakeholders avoid making choices that could bring about catastrophic defect numbers at the end or at least be prepared for it. In this paper, six requirements-related attributes are analyzed to discover if they can be used for determining the occurrences of requirements-related defects. We measured two types of attributes: point and aggregate. The point attributes include time estimates, priority and ownership. The aggregate attributes include the number of indirect stakeholders, the number of related stories and the story creation time. Our analysis is based on data from the development of the IBM Jazz system. Our result shows that the number of indirect stakeholders and the number of related stories are good predictors for the number of defects, but other attributes show no or little correlation with the defects.

[1]  Kristin A. Farry,et al.  Customer Centered Products: Creating Successful Products Through Smart Requirements Management (Продукты для клиента: создание успешных продуктов с помощью грамотного управления требованиями) , 2000 .

[2]  Taghi M. Khoshgoftaar,et al.  Detection of software modules with high debug code churn in a very large legacy system , 1996, Proceedings of ISSRE '96: 7th International Symposium on Software Reliability Engineering.

[3]  Li-Te Cheng,et al.  Building Collaboration into IDEs , 2003, ACM Queue.

[4]  Randall Frost,et al.  Jazz and the Eclipse Way of Collaboration , 2007, IEEE Software.

[5]  Barry W. Boehm,et al.  Software Defect Reduction Top 10 List , 2001, Computer.

[6]  Nachimuthu Karunanithi.  A neural network approach for software reliability growth modeling in the presence of code churn , 1993, Proceedings of 1993 IEEE International Symposium on Software Reliability Engineering.

[7]  Elliot Soloway,et al.  Where the bugs are , 1985, CHI '85.

[8]  Qaiser S. Durrani,et al.  A study to investigate the impact of requirements instability on software defects , 2004, SOEN.

[9]  Didar Zowghi,et al.  A study of the impact of requirements volatility on software project performance , 2002, Ninth Asia-Pacific Software Engineering Conference, 2002..

[10]  N. Nagappan,et al.  Use of relative code churn measures to predict system defect density , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[11]  Capers Jones.  Software quality - analysis and guidelines for success , 1997 .

[12]  Gary E. Mogyorodi.  What Is Requirements-Based Testing? , 2003 .

[13]  Nachiappan Nagappan,et al.  Predicting defects using network analysis on dependency graphs , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[14]  Robert L. Mason,et al.  Statistical Principles in Experimental Design , 2003 .

[15]  Sebastian G. Elbaum,et al.  Code churn: a measure for estimating the impact of code change , 1998, Proceedings. International Conference on Software Maintenance (Cat. No. 98CB36272).

[16]  Barry Boehm,et al.  Top 10 list [software development] , 2001 .

[17]  Betty H. C. Cheng,et al.  Research Directions in Requirements Engineering , 2007, Future of Software Engineering (FOSE '07).

[18]  Li-Te Cheng,et al.  Jazzing up Eclipse with collaborative tools , 2003, eclipse '03.

[19]  Richard E. Fairley,et al.  Software engineering concepts , 1985, McGraw-Hill series in software engineering and technology.

[20]  David J. Hand,et al.  Statistics and data mining: intersecting disciplines , 1999, SKDD.

[21]  Daniel Port,et al.  Comparative studies of the model evaluation criterions mmre and pred in software cost estimation research , 2008, ESEM '08.

[22]  Witold Pedrycz,et al.  A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[23]  G. Gurtner,et al.  Statistics in medicine. , 2011, Plastic and reconstructive surgery.

[24]  Chris Arney,et al.  Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life , 2009 .

[25]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .

[26]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .