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Defect prevention with orthogonal defect classification

Published: 23 February 2009 Publication History

Abstract

"Learning from the past" to make systems more efficient in terms of cost and time is the hallmark of any engineering discipline. Prevention of defects is the "holy grail" of "learning's from the past" i.e. every product generation we learn from our defects and prevent it from recurring in the next generation. Taking this philosophy further "capturing defects in the earlier stage of the life cycle" is a means of preventing defects in the later stages of the product life cycle. Correction of defects is costly and the cost increases exponentially with every subsequent stage. This also impacts the cycle-time directly. Defect prevention then not only helps in cost reduction but also helps in cutting down the development time.
When we talk of software development, we are talking of hundreds of defects (in-process as well as post release). It would be very inefficient and time-consuming to have preventive action planning for each of them, as most of them could be symptoms of some common root cause. If we have to handle software defects of such large magnitude the best way then would be to classify them into patterns and then do root cause analysis on those patterns.
There are number of methodologies available in the industry to do this classification and IBM's ODC (Orthogonal defect classification) is one such methodology. The methodology classifies each defect into orthogonal (mutually exclusive) attributes some technical and some managerial. These attributes provide all the information to be able to sift through the enormous volume of data and arrive at patterns on which root-cause analysis can be done. This coupled with good action planning and tracking can achieve high degree of defect reduction and cross learning.
The questions always then are -- can methodologies be really applied to do software defect prevention in a structured way? How can an abstract defect classification mechanism really help to identify patterns? How does one measure the effectiveness of such actions? Who should do these activities? How does all this fit into the existing process framework? This paper is an attempt to answer these questions by sharing the experiences of using the IBM-ODC methodology in a case study of real-life software development project (DVD player product). The motivation of this paper is not to discuss the ODC methodology itself but rather to demonstrate through a case study, the structured process for defect prevention using some attributes of ODC for defect classification and its related interpretations for causal analysis, action planning and results tracking. The fitments of the scheme into the bigger picture of defect prevention, cross learning and mapping to elements of the SEI-CMMI framework are some of the highlights of this paper. The paper concludes with a summary of learning's and some points to ponder.

References

[1]
IBM Research -- Centre for Software engineering http://researchweb.watson.ibm.com/softeng/ODC/DETODC.v5.1
[2]
John T Huber, Hewlett Packard Company, A Comparison of IBM's Orthogonal Defect Classification to Hewlett Packard's Defect Origins, Types, and Modes, http://www.stickyminds.com/sitewide.asp?Function=edetail&ObjectType=ART&ObjectId=2883
[3]
Chillarege, R., Bhandari, I. S., Chaar, J. K., Halliday, M. J., Moebus, D. S., Ray, B. K. & Wong, M., Orthogonal Defect Classification: Concept for In-Process Measurement, IEEE. Transaction on Software Engineering, Vol. 18 No. 11, Nov-92 http://www.chillarege.com/odc/articles/odcconcept/odc.html
[4]
CMMI® for development v1.2. August 2006 Reference: CMU/SEI-2006-R-008 ESC-R-2006-008CMMI Product team

Cited By

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  • (2022)Study of the Software Development Life Cycle and the Function of Testing2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)10.1109/IIHC55949.2022.10060231(1270-1275)Online publication date: 18-Nov-2022
  • (2021)Contemporary COBOL: Developers' Perspectives on Defects and Defect Location2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME52107.2021.00027(227-238)Online publication date: Sep-2021
  • (2021)Improved prediction of software defects using ensemble machine learning techniquesNeural Computing and Applications10.1007/s00521-021-05811-3Online publication date: 2-Mar-2021
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cover image ACM Conferences
ISEC '09: Proceedings of the 2nd India software engineering conference
February 2009
154 pages
ISBN:9781605584263
DOI:10.1145/1506216
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 February 2009

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Author Tags

  1. algorithm
  2. assignment
  3. car
  4. checking
  5. documentation
  6. function
  7. interface
  8. odc
  9. timing

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ISEC '09
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ISEC '09: India Software Engineering Conference
February 23 - 26, 2009
Pune, India

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Overall Acceptance Rate 76 of 315 submissions, 24%

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Cited By

View all
  • (2022)Study of the Software Development Life Cycle and the Function of Testing2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)10.1109/IIHC55949.2022.10060231(1270-1275)Online publication date: 18-Nov-2022
  • (2021)Contemporary COBOL: Developers' Perspectives on Defects and Defect Location2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME52107.2021.00027(227-238)Online publication date: Sep-2021
  • (2021)Improved prediction of software defects using ensemble machine learning techniquesNeural Computing and Applications10.1007/s00521-021-05811-3Online publication date: 2-Mar-2021
  • (2021)Stacking Based Ensemble Learning for Improved Software Defect PredictionProceeding of Fifth International Conference on Microelectronics, Computing and Communication Systems10.1007/978-981-16-0275-7_14(167-178)Online publication date: 10-Sep-2021
  • (2020)Supervised machine learning approach to predict qualitative software productEvolutionary Intelligence10.1007/s12065-020-00434-4Online publication date: 9-Jun-2020
  • (2018)Causal Analysis and Resolution with Theory of ConstraintsProceedings of the XVII Brazilian Symposium on Software Quality10.1145/3275245.3275252(61-70)Online publication date: 17-Oct-2018
  • (2018)Challenges of software process and product quality improvementSoftware Quality Journal10.1007/s11219-016-9334-626:2(779-807)Online publication date: 1-Jun-2018
  • (2014)Toward Extended Change Types for Analyzing Software FaultsProceedings of the 2014 14th International Conference on Quality Software10.1109/QSIC.2014.10(202-211)Online publication date: 2-Oct-2014
  • (2014)Operational Profile Modeling as a Risk Assessment Tool for Software Quality TechniquesProceedings of the 2014 International Conference on Computational Science and Computational Intelligence - Volume 0210.1109/CSCI.2014.115(181-184)Online publication date: 10-Mar-2014
  • (2013)Using Process Enactment Data Analysis to Support Orthogonal Defect Classification for Software Process ImprovementProceedings of the 2013 Joint Conference of the 23nd International Workshop on Software Measurement (IWSM) and the 8th International Conference on Software Process and Product Measurement10.1109/IWSM-Mensura.2013.27(120-125)Online publication date: 23-Oct-2013
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