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Factors of software quality - analysis of extended ISBSG dataset

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Warianty tytułu
Języki publikacji
PL
Abstrakty
EN
In this paper, we analyze the extended ISBSG dataset, which contains data on a wide range of software projects developed in various companies worldwide. The main aim of this paper is to identify important factors that influence software quality and to investigate the nature of these relationships. This analysis involves using various statistical techniques, both analytical and graphical. We provide a rating for each variable to express the strength of its relationship with software quality. Unlike earlier analyses, we focus on the business perspective and its relationships on software quality. Obtained results may be used do support decision making in software projects, specifically by demonstrating the impact of selected software development practices.
Rocznik
Strony
293--313
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
  • Department of Information Systems Engineering, University of Szczecin, ul. Mickiewicza 64, 71-101 Szczecin, Poland, lukrad@uoo.univ.szczecin.pl
Bibliografia
  • [1] Agrawal M., Chari K., Software Effort, Quality, and Cycle Time: A Study of CMM Level 5 Projects, IEEE Transactions on Software Engineering. 33, 3, 2007, 145-156.
  • [2] Azzeh M., Neagu D., Cowling P. I., Fuzzy grey relational analysis for software effort estimation, Empirical Software Engineering, 15, 1, 2010, 60-90.
  • [3] Briand L.C., Freimut B., Vollei F., Assessing the Cost-Effectiveness of Inspections by Combining Project Data and Expert Opinion, in: Proceedings of the 11th International Symposium on Software Reliability Engineering (ISSRE '00), IEEE Computer Society, Washington, DC, USA, 2000, 124.
  • [4] Clark P., Niblett T., The CN2 Induction Algorithm, Machine Learning, 3, 4, 1989, 261-283.
  • [5] Fenton N.E., Pfleeger S.L., Software Metrics. A Rigorous and Practical Approach, PWS Publishing Company, Boston, 1997.
  • [6] Hall T., Fenton N., Implementing Effective Software Metrics Programs, IEEE Software 14, 2, 1997, 55-65.
  • [7] ISBSG Repository Data Release 11, International Software Benchmarking Standards Group, 2009, www.isbsg.org.
  • [8] ISBSG, ISBSG Comparative Estimating Tool V4.0 - User Guide, International Software Benchmarking Standards Group, 2005, www.isbsg.org.
  • [9] Jones C., Applied Software Measurement: Global Analysis of Productivity and Quality, Third Edition, McGraw-Hill, New York, 2008.
  • [10] Kan S. H., Metrics and Models in Software Quality Engineering, Addison-Wesley, Boston, 2003.
  • [11] Liu Q., Qin W. Z., Mintram R., Ross M., Evaluation of preliminary data analysis framework in software cost estimation based on ISBSG R9 Data, Software Quality Journal, 16, 3, 2008, 411-458.
  • [12] Lyu M., Handbook of software reliability engineering, McGraw-Hill, Hightstown, NJ, 1996.
  • [13] Maxwell K.D., Applied Statistics for Software Managers, Prentice Hall PTR, Upper Saddle River, NJ, 2002.
  • [14] Mendes E., Lokan C., Replicating studies on cross- vs single-company effort models using the ISBSG Database, Empirical Software Engineering, 13, 1, 2008, 3-37.
  • [15] Munson J.C., Nikora A.P., Sherif J.S., Software faults: a quantifiable definition, Advances in Engineering Software, 37, 5, 2006, 327-333.
  • [16] Orange, Laboratory of Artificial Intelligence, Faculty of Computer and Information Science, University of Ljubljana, Slovenia, 2010, http://orange.biolab.si.
  • [17] Radliński Ł., Fenton N., Marquez D., Estimating Productivity and Defect Rates Based on Environmental Factors, in: Information Systems Architecture and Technology: Models of the Organisation's Risk Management, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław, 2008, 103-113.
  • [18] Radliński Ł., Predicting Defect Types in Software Projects, Polish Journal of Environmental Studies, 18, 3B, 2009, 311-315.
  • [19] Robinson B., Francis P., Ekdahl F., A defect-driven process for software quality improvement, in: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (ESEM '08), ACM, New York, NY, USA, 2008, 333-335.
  • [20] Schulmeyer G.G., McManus J.I. (eds.), Handbook of Software Quality Assurance, Prentice Hall PTR, Upper Saddle River, NJ, 1999.
  • [21] StatSoft, Inc., STATISTICA (data analysis software system), version 9.1, 2010, www.statsofl.com.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP2-0019-0070
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