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Predicting the Flow of Defect Correction Effort using a Bayesian Network Model

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Abstract

The number of defects alone does not provide software companies with enough information on the effort required to fix them. Defects have different impacts on the overall defect correction effort – defects introduced in one phase may be found and corrected in the same or later phase. The later they are found, the more effort is required to correct them. The main aim of this paper is to build and validate a model (Bayesian Network) for predicting the defect correction effort at various phases of the software development process. The procedure of building the model covers the following steps: problem analysis, data analysis, model definition and enhancement, simulation runs, and model validation. Developed Defect Cost Flow Model (DCFM), which is an implementation of the V-model of a software project lifecycle, correctly incorporates known qualitative and quantitative relationships. Application of DCFM in a real industrial process revealed its high potential in finding the appropriate amount of review effort for specific development phases to minimize the overall costs. The model may be used in the industry for decision support. It can be extended and calibrated to meet the needs of specific development environment.

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Abbreviations

BN:

Bayesian Network

CPT:

Conditional Probability Table

DCE:

Defect Correction Effort

DCF:

Defect Cost Factor

DCFM:

Defect Cost Flow Model

DE:

Design Stage

IM:

Implementation Stage

I&T:

Integration and Testing Stage

KPI:

Key Performance Indicator

QA:

Quality Assurance

RE:

Requirements Engineering Stage

References

  • Abouelela M, Benedicenti L (2010) Bayesian Network Based XP Process Modelling. Int J Softw Eng Appl 1:1–15

    Article  Google Scholar 

  • AgenaRisk BN Tool (2009) Agena, http://www.agenarisk.com/

  • Antoniol G, Di Penta M, Harman M (2011) The use of Search-Based Optimization Techniques to Schedule and Staff Software Projects: an Approach and an Empirical Study. Softw Pract Exp (in press)

  • Basili VR, Rombach HD (1988) The TAME project: Towards improvement-oriented software environments. IEEE Trans Softw Eng 14:758–773

    Article  Google Scholar 

  • Bibi S, Stamelos I (2004) Software Process Modeling with Bayesian Belief Networks. Proc. Int, Software Metrics Symposium, Chicago

    Google Scholar 

  • Boetticher G, Menzies T, Ostrand T (2007) PROMISE Repository of empirical software engineering data. West Virginia University, Department of Computer Science, http://promisedata.org/?p=224

  • Chillarege R, Bhandari IS, Chaar JK, Halliday MJ, Moebus DS, Ray BK, Wong MY (1992) Orthogonal Defect Classification-A Concept for In-Process Measurements. IEEE Trans Softw Eng 18:943–956

    Article  Google Scholar 

  • CMMI Product Team (2010) CMMI for Development, Version 1.3. Technical Report CMU/SEI-2010-TR-033. Software Engineering Institute (SEI)

  • Dabney JB, Barber G, Ohi D (2006) Predicting Software Defect Function Point Ratios Using a Bayesian Belief Network. Proc. 2nd Int. Workshop on Predictor Models in Software Engineering, Philadelphia

  • Das B (2004) Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem, http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:cs/0411034

  • De Melo ACV, Sanchez AJ (2008) Software maintenance project delays prediction using Bayesian Networks. Exp Syst Appl 34:908–919

    Article  Google Scholar 

  • Del Salgado MJ, del Aguina Cano IM (2008) A Bayesian Network for Predicting the Need for a Requirements Review. In: Meziane F, Vadera S (eds) Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects. Information Science Reference, New York, pp 106–128

    Google Scholar 

  • Durillo JJ, Zhang YY, Alba E, Nebro AJ (2009) A Study of the Multi-objective Next Release Problem. Proc. 2009 1st International Symposium on Search Based Software Engineering (SSBSE '09). IEEE CS, Washington, DC, pp 49–58

    Book  Google Scholar 

  • Fenton N, Hearty P, Neil M, Radliński Ł (2008a) Software Project and Quality Modelling Using Bayesian Networks”. In: Meziane F, Vadera S (eds) Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects. Information Science Reference, New York, pp 1–25

    Google Scholar 

  • Fenton N, Marsh W, Neil M, Cates P, Forey S, Tailor M (2004) Making Resource Decisions for Software Projects. Proc. 26th Int. Conference on Software Engineering, Washington DC, pp 397–406

    Google Scholar 

  • Fenton N, Neil M, Marsh W, Hearty P, Radliński Ł, Krause P (2008b) On the effectiveness of early life cycle defect prediction with Bayesian Nets. Empir Softw Eng 13:499–537

    Article  Google Scholar 

  • Fenton NE, Neil M, Caballero JG (2007a) Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks. IEEE Trans Knowl Data Eng 19:1420–1432

    Article  Google Scholar 

  • Fenton NE, Neil M, Marsh W, Krause P, Mishra R (2007b) Predicting Software Defects in Varying Development Lifecycles using Bayesian Nets. Inf Softw Technol 43:32–43

    Article  Google Scholar 

  • Finkelstein A, Harman M, Mansouri S, Ren J, Zhang YY (2009) A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making. Requir Eng 14:231–245

    Article  Google Scholar 

  • Gueorguiev S, Harman M, Antoniol G (2009) Software project planning for robustness and completion time in the presence of uncertainty using multi objective search based software engineering. Proc. 11th Annual conference on Genetic and evolutionary computation (GECCO '09). ACM, New York, pp 1673–1680

    Google Scholar 

  • Hearty P, Fenton N, Marquez D, Neil M (2009) Predicting Project Velocity in XP using a Learning Dynamic Bayesian Network Model. IEEE Trans Softw Eng 37:124–137

    Article  Google Scholar 

  • Helsper EM, van der Gaag LC, Feelders AJ, Loeffen WLA, Geenen PL, Albers ARW (2005) Bringing Order into Bayesian-Network Construction. Proc. 3rd Int. Conf. on Knowledge Capture. Banff, Alberta, pp 121–128

    Google Scholar 

  • Herbsleb J (2010) MSR: Mining for scientific results?. 2010 7th IEEE Working Conference on Mining Software Repositories. IEEE, Cape Town, pp 1

  • IABG (1992) The V-Model—General Directive 250. Software Development Standard for the German Federal Armed Forces, Industrieanlagen-Betriebsgesellschaft (IABG), Ottobrunn, Germany

    Google Scholar 

  • Jensen FV, Nielsen TD (2007) Bayesian Networks and Decision Graphs, 2nd edn. Springer

  • Jørgensen M, Shepperd M (2007) A Systematic Review of Software Development Cost Estimation Studies. IEEE Trans Softw Eng 33:33–53

    Article  Google Scholar 

  • Kjærulff UB, Madsen AL (2008) Bayesian Networks and Influence Diagrams. A Guide to Construction and Analysis. Springer, New York

    MATH  Google Scholar 

  • Klaes M, Beletski T, Sarishvili A (2007)AP 3.1: Effektivität von QS-Maßnahmen Stand der Wissenschaft. Fraunhofer IESE-Report 096.07/D, Kaiserslautern

  • Kraaijeveld P, Druzdzel M, Onisko A, Wasyluk H (2005) GeNIeRate: An Interactive Generator of Diagnostic Bayesian Network Models. Working Notes of the 16th International Workshop on Principles of Diagnosis (DX-05), Monterey, CA, pp 175–180

    Google Scholar 

  • Kruchten P (1998) The Rational Unified Process: An Introduction. Addison-Wesley

  • Lee I, Leung JYT, Son SH (2007) Handbook of Real-Time and Embedded Systems. Chapman & Hall/CRC

  • Mair C, Kadoda G, Lefley M, Phalp K, Schofield C, Shepperd M, Webster S (2000) An investigation of machine learning based prediction systems. J Syst Softw 53:23–29

    Article  Google Scholar 

  • Mendes E, Mosley N (2008) Bayesian Network Models for Web Effort Prediction: A Comparative Study. IEEE Trans Softw Eng 34:723–737

    Article  Google Scholar 

  • Nadkarni S, Shenoy PP (2004) A Causal Mapping Approach to Constructing Bayesian Networks. Decis Support Syst 38:259–281

    Article  Google Scholar 

  • Neil M, Marquez D, Fenton NE (2010) Improved Reliability Modeling using Bayesian Networks and Dynamic Discretization. Reliab Eng Syst Saf 95:412–425

    Article  Google Scholar 

  • Neil M, Tailor M, Marquez D (2007) Inference in Hybrid Bayesian Networks using Dynamic Discretisation. Stat Comput 17:219–233

    Article  MathSciNet  Google Scholar 

  • O’Hagan A, Buck CE, Daneshkhah A, Eiser JE, Garthwaite PH, Jenkinson DJ, Oakley JE, Rakow T (2006) Uncertain Judgements: Eliciting Expert Probabilities. John Wiley and Sons, Chichester

    Book  MATH  Google Scholar 

  • Pearl J (1985) Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning. Proc. 7th Conf. of the Cognitive Science Society. University of California, pp 329–334

  • Pearl J (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Pendharkar PC, Subramanian GH, Rodger JA (2005) A Probabilistic Model for Predicting Software Development Effort. IEEE Trans Softw Eng 31:615–624

    Article  Google Scholar 

  • Pfautz J, Cox Z, Catto G, Koelle D, Campolongo J, Roth E (2007) User-Centered Methods for Rapid Creation and Validation of Bayesian Belief Networks. Proc. 5th Bayesian Modeling Applications Workshop, 23rd Annual Conference on Uncertainty in Artificial Intelligence. Vancouver, British Columbia

  • Radlinski L (2008) Improved Software Project Risk Assessment Using Bayesian Nets. Ph.D. Thesis. Queen Mary University, London

  • Radlinski L (2010) A Survey of Bayesian Net Models for Software Development Effort Prediction. Int J Softw Eng Comput 2:95–109

    Google Scholar 

  • Radliński Ł (2010) Software Development Effort and Quality Prediction Using Bayesian Nets and Small Local Qualitative Data. Proc. 22nd International Conference on Software Engineering and Knowledge Engineering. Redwood City, CA, pp. 113–116

  • Radliński Ł, Fenton N, Neil M (2008) A Learning Bayesian Net for Predicting Number of Software Defects Found in a Sequence of Testing. Pol J Env Stud 17:359–364

    Google Scholar 

  • Radliński Ł, Fenton N, Neil M, Marquez D (2007) Improved Decision-Making for Software Managers Using Bayesian Networks. Proc. 11th IASTED Int. Conf. Software Engineering and Applications. IASTED, Cambridge, pp 13–19

    Google Scholar 

  • Schulz T, Radliński LŁ, Gorges T, Rosenstiel W (2010) Defect Cost Flow Model—A Bayesian Network for Predicting Defect Correction Effort. Proc. 6th International Conference on Predictive Models in Software Engineering. ACM, New York

    Google Scholar 

  • Schulz T, Radliński Ł, Gorges T, Rosenstiel W (2011) Software Process Model using Dynamic Bayesian Networks. In: Ramachandran M (ed) Knowledge Engineering for Software Development Life Cycles: Support Technologies and Applications. Information Science Reference, Hershey, pp 289–310

    Chapter  Google Scholar 

  • Skaanning C (2000) A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters. Proc. 16th Conference on Uncertainty in Artificial Intelligence. Stanford University, Stanford, CA

    Google Scholar 

  • Song Q, Jia Z, Shepperd M, Ying S, Liu J (2011) A General Software Defect-Proneness Prediction Framework. IEEE Trans Softw Eng. doi:10.1109/TSE.2010.90

  • Stamelos I, Angelis L, Dimou P, Sakellaris E (2003) On the use of Bayesian belief networks for the prediction of software productivity. Inf Softw Technol 45:51–60

    Article  Google Scholar 

  • Stewart B (2002) Predicting project delivery rates using the Naive–Bayes classifier. J Softw Maint Evol Res Pract 14:161–179

    Article  MATH  Google Scholar 

  • Stolz W, Wagner P (2004) Introduction of a quantitative Quality Management for the ECU Software development at Gasoline Systems. Proceedings of the 2nd Bosch Conference on Software Engineering (BoCSE-2004), Ludwigsburg, Germany, pp 25–27

    Google Scholar 

  • Torkar R, Adnan NM, Alvi AK, Afzal W (2010) Predicting software test effort in iterative development using a dynamic Bayesian network. Proc. of 21st IEEE International Symposium on Software Reliability Engineering, Industry Practice Track. San Jose

  • Van Koten C, Gray AR (2006) An application of Bayesian network for predicting object-oriented software maintainability. Inf Softw Technol 48:59–67

    Article  Google Scholar 

  • Wagner S (2009) A Bayesian network approach to assess and predict software quality using activity-based quality models. Proc. 5th Int. Conf. on Predictor Models in Software Engineering. ACM Press, New York

    Google Scholar 

  • Wang H, Peng F, Zhang C, Pietschker A (2006) Software Project Level Estimation Model Framework based on Bayesian Belief Networks. Proc 6th Int. Conf. on Quality Software. IEEE CS, Washington, DC, pp 209–218

    Google Scholar 

  • Wang X, Wu C, Ma L (2010) Software project schedule variance prediction using Bayesian Network. Proc. 2010 IEEE International Conference on Advanced Management Science. Chengdu, pp 26–30

  • Weiss SM (1984) A Practical Guide to Designing Expert Systems. Rowman & Littlefield Publishers

  • Wiegmann DA (2005) Developing a Methodology for Eliciting Subjective Probability Estimates During Expert Evaluations of Safety Interventions: Application for Bayesian Belief Networks. Rep. No. AHFD-05-13/NASA-05-4). Aviation Human Factors Division Institute of Aviation, University of Illinois

  • Winkler RL (2003) An introduction to Bayesian inference and decision, 2nd edn. Probabilistic Publishing, Gainesville

    Google Scholar 

  • Wooff DA, Goldstein M, Coolen FPA (2002) Bayesian Graphical Models for Software Testing. IEEE Trans Softw Eng 28:510–525

    Article  Google Scholar 

  • Zhang D, Tsai JJP (2003) Machine Learning and Software Engineering. Softw Qual J 11:87–119

    Article  Google Scholar 

  • Zhou Y, Würsch M, Giger E, Gall HC, Lü J (2008) A Bayesian Network Based Approach for Change Coupling Prediction. Proc. 15th Working Conference on Reverse Engineering. IEEE CS, Washington DC, pp 27–36

    Google Scholar 

  • Zimmermann K, Hauser HM (2004) The growing importance of embedded software: Managing hybrid hardware-software business. Technical Report. The Boston Consulting Group

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Authors and Affiliations

  1. Robert Bosch GmbH, Car Multimedia (CM-IS/EBM1), Postfach 16 61, 71226, Leonberg, Germany

    Thomas Schulz

  2. Department of Information Systems Engineering, University of Szczecin, ul. Mickiewicza 64, 71-101, Szczecin, Poland

    Łukasz Radliński

  3. Robert Bosch GmbH, CC/ESR2, Postfach 16 61, 71226, Leonberg, Germany

    Thomas Gorges

  4. Eberhard Karls Universität Tübingen, Sand 13, B 207, 72076, Tübingen, Germany

    Wolfgang Rosenstiel

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  1. Thomas Schulz
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  2. Łukasz Radliński
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  4. Wolfgang Rosenstiel
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Correspondence to Thomas Schulz.

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Editor: Tim Menzies and Gunes Koru

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Schulz, T., Radliński, Ł., Gorges, T. et al. Predicting the Flow of Defect Correction Effort using a Bayesian Network Model. Empir Software Eng 18, 435–477 (2013). https://doi.org/10.1007/s10664-011-9175-7

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