The Jean-Claude Laprie Award in Dependable Computing is awarded annually since 2012 by the IFIP Working Group 10.4 on Dependable Computing and Fault Tolerance in his honor. The award recognizes outstanding papers that have significantly influenced the theory and/or practice of Dependable Computing. It takes the form of a memorial plaque presented to the author(s) at the Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).
Any paper relating to dependable and secure computing, and published at least 10 years prior to the award year (e.g., 2016 or earlier for the 2026 award) is eligible for the award.
The award seeks to recognize papers that have had a significant impact in the intervening years in one or more of the three following categories:For 2026, the Award Committee has unanimously decided to select the following paper:
Paper: Orthogonal Defect Classification â A Concept for In-Process Measurements
Authors: Ram Chillarege, Inderpal S. Bhandari, Jarir K. Chaar, Michael J. Halliday, Diane S. Moebus, Bonnie K. Ray, and Man-Yuen Wong
Journal: IEEE Transactions on Software Engineering
DOI: https://doi.org/10.1109/32.177364
The Award Citation:
âOrthogonal Defect Classification â A Concept for In-Process Measurementsâ, by Ram Chillarege, Inderpal S. Bhandari,
Jarir K. Chaar, Michael J. Halliday, Diane S. Moebus, Bonnie K. Ray, and Man-Yuen Wong, was the first paper to
demonstrate how defects can be used as inâprocess measurements to make software development measurable and
controllable. At the time of publication, there was a challenging gap between statistical modeling, which often occurred
too late in the development process, and causal analysis techniques that were costly and unsuitable for quantitative
analysis. The paper bridged the gap by introducing a classification scheme and a corresponding framework for defect
types and defect triggers, making it easy for software engineers to correctly identify the most likely class. Each
development process tends to exhibit a particular defect-type distribution for each process phase. Measuring and
classifying defect types thus also allows for the quick identification of divergence between the actual development and
the intended development process. A similar effect is described for defect triggers, conditions that allow defects to surface.
The paper demonstrated the classification and measurement framework on several industrial use cases. Since its
publication, Orthogonal Defect Classification (ODC) has had a tremendous impact on industry, driving improvements in
efficiency and reliability. Indeed, it has demonstrated significant, sometimes dramatic improvements in productivity and
efficiency, and many companies have highlighted the impact of ODC on their developments. Among others, these
companies are IBM, Nortel, and Motorola. As ODC was adopted across many organizations worldwide, the cumulative
effect on efficiency across organizations and projects was substantial. ODC has also had a significant scientific impact,
with about 1,200 citations according to Google Scholar. As software engineering paradigms shifted from Waterfall to
Spiral and then to Agile, ODC continued to work, underscoring its foundational character. More recently, machine
learning research has leveraged the structure and reasoning models ODC established. For these reasons, the award
Committee decided to select this paper as the 2026 JCL award winner.
Wilfried Steiner, TTTech Computertechnik AG, Austria
Citations and complete information on the Jean-Claude Laprie awards can be found on the award web page: http://jclaprie-award.dependability.org/