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An empirical study of defects in industrial projects

An empirical study of defects in industrial projects. PhD-seminar 23. November 2004 Jon Arvid Børretzen. Relation to BUCS. This investigation aims at doing analysis on projects with respect to metrics and attributes that are relevant to BUCS.

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An empirical study of defects in industrial projects

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  1. An empirical study of defects in industrial projects PhD-seminar23. November 2004 Jon Arvid Børretzen

  2. Relation to BUCS This investigation aims at doing analysis on projects with respect to metrics and attributes that are relevant to BUCS. In business-critical systems, the critical part is regularly related to the dependability of the system. I will be trying to make comparisons between component-based systems and more traditional architecture systems.

  3. An empirical analysis of defects in industrial projects This empirical study is planned to look at how defects have been introduced into the system, and how they have been found and dealt with. By analysing defect-/change reports for several (semi-)completed development projects, we want to investigate if there are common causes for defects being introduced in systems and/or not being discovered earlier.

  4. Defects, errors and failures • Defects (or faults) are potential flaws in a hardware/software system, that later may be activated to produce an error. • An error is the execution of a "passive fault", leading to erroneous behaviour or system state. • A failure is a fault execution (an error) that results in observable and erroneous external behaviour.

  5. Data material • Fault reports • Design documents • Relevant code

  6. What do we hope to learn? • Why did we make these mistakes in the first place? • Why did we not find them earlier (inspection, testing)?

  7. Defect categorisation • Type of defect () • Severity (negligible  severe) • System component • Origin of work (reuse, cots, in-house) • Life-cycle phase for defect (where the defect was introduced) • (concept  maintenance) • more likely (analysis  operation) • Phase of discovery • Reason for not discovering earlier

  8. Analysis steps • 1. Separate actual faults/defects from “cosmetic” changes • 1b. Sort out duplicate defect reports • 2. Categorise defects according to criteria • 3. Analyse results • 4. Generalise results • 5. Describe what we can learn

  9. Generalising results (problem) • Just a few projects to study • Differences in • Domain • Environment • Programming environment/style • Defect/failure report data • How to make findings interesting in general, not just to the one project

  10. Categorisation issues • What do we consider to be a fault? • Design faults? • Wrong implementation choices? • Changes in specification? • Changes in design?

  11. To sum up • Empirical study of industrial projects • Analysis of “historical data” in form of fault reports • Categorisation of defects • Relate this to dependability/reliability

  12. Questions • What should be considered as defects/faults? • What about design defects? • How can knowledge about system defects improve the software process? • How to show improvement effects for projects only with study result without executing change actions? • Or will it only be ideas for improvement?

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