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Are delayed issues harder to resolve? Revisiting cost-to-fix of defects throughout the lifecycle

Abstract

Many practitioners and academics believe in a delayed issue effect (DIE); i.e. the longer an issue lingers in the system, the more effort it requires to resolve. This belief is often used to justify major investments in new development processes that promise to retire more issues sooner. This paper tests for the delayed issue effect in 171 software projects conducted around the world in the period from 2006–2014. To the best of our knowledge, this is the largest study yet published on this effect. We found no evidence for the delayed issue effect; i.e. the effort to resolve issues in a later phase was not consistently or substantially greater than when issues were resolved soon after their introduction. This paper documents the above study and explores reasons for this mismatch between this common rule of thumb and empirical data. In summary, DIE is not some constant across all projects. Rather, DIE might be an historical relic that occurs intermittently only in certain kinds of projects. This is a significant result since it predicts that new development processes that promise to faster retire more issues will not have a guaranteed return on investment (depending on the context where applied), and that a long-held truth in software engineering should not be considered a global truism.

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Notes

  1. Endres & Rombach note that these are not laws of nature in the scientific sense, but theories with repeated empirical evidence.

  2. For example, popular sources such as Pressman (2005), Boehm and Basili (2001), Glass (2002), and Endres and Rombach (2003), with a combined citation count of over 14,500 on Google Scholar, can all trace their evidence to Software Engineering Economics (Boehm 1981).

  3. We use the RqtsErr formulation since this issue typically needs no supportive explanatory text. If we had asked respondents about our more general term “delayed issue effect”, we would have had to burden our respondents with extra explanations.

  4. We selected 30 for this threshold via the central limit theorem (Maxwell 2002).

  5. Recall that in a sorted list of numbers, the inter-quartile range, or IQR, is the difference between the 75th and 25th percentile value.

  6. In retrospect, empirical software engineering studies at that time were extremely rare, and guidance for reporting empirical case studies and experiments have improved substantially. One of the seminal books on quasi-experimentation and reporting of validity concerns, Cook and Campbell (1979), had not been published when most of the DIE papers were written.

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Acknowledgments

The authors wish to thank David Tuma and Yasutaka Shirai for their work on the SEI databases that made this analysis possible. In particular, we thank Tuma Solutions for providing the Team Process Data Warehouse software. Also, the authors gratefully acknowledge the careful comments of anonymous reviewers from the FSE and ICSE conferences. This work was partially funded by an National Science Foundation grants NSF-CISE 1302169 and CISE 1506586.

This material is based upon work funded and supported by TSP Licensing under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center sponsored by the United States Department of Defense. This material has been approved for public release and unlimited distribution. DM-0003956

Personal Software Process SM, Team Software Process SM, and TSP SM are service marks of Carnegie Mellon University.

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Correspondence to Tim Menzies.

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Communicated by: Per Runeson

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Menzies, T., Nichols, W., Shull, F. et al. Are delayed issues harder to resolve? Revisiting cost-to-fix of defects throughout the lifecycle. Empir Software Eng 22, 1903–1935 (2017). https://doi.org/10.1007/s10664-016-9469-x

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Keywords

  • Software economics
  • Phase delay
  • Cost to fix