The Jean-Claude Laprie Award

Jean-Claude Laprie

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:

Winner of the 2026 Jean-Claude Laprie Award

For 2026, the Award Committee has unanimously decided to select the following paper:

🏆 2026 Jean-Claude Laprie Award Winner

Paper: Orthogonal Defect Classification – 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:
“Orthogonal Defect Classification – A Concept for In-Process Measurements”, 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 in‐process 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.

DSN-2026 Jean-Claude Laprie award Committee

Chair:

Wilfried Steiner, TTTech Computertechnik AG, Austria

Members:

Citations and complete information on the Jean-Claude Laprie awards can be found on the award web page: http://jclaprie-award.dependability.org/