Software errors and complexity: an empirical investigation0
Communications of the ACM
Collecting and categorizing software error data in an industrial environment
Journal of Systems and Software - Special issue on the fifth Minnowbrook workshop on software performance evaluation
Orthogonal Defect Classification-A Concept for In-Process Measurements
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Measuring Process Consistency: Implications for Reducing Software Defects
IEEE Transactions on Software Engineering
A case study in root cause defect analysis
Proceedings of the 22nd international conference on Software engineering
A Comparative Study of Ordering and Classification of Fault-ProneSoftware Modules
Empirical Software Engineering
Learning from Our Mistakes with Defect Causal Analysis
IEEE Software
Software Faults in Evolving a Large, Real-Time System: a Case Study
ESEC '93 Proceedings of the 4th European Software Engineering Conference on Software Engineering
Empirical Analysis of Safety-Critical Anomalies During Operations
IEEE Transactions on Software Engineering
Toward Understanding the Rhetoric of Small Source Code Changes
IEEE Transactions on Software Engineering
Defect prevention in software processes: An action-based approach
Journal of Systems and Software
Improvement of causal analysis using multivariate statistical process control
Software Quality Control
A model for software rework reduction through a combination of anomaly metrics
Journal of Systems and Software
Defect categorization: making use of a decade of widely varying historical data
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Integrating in-process software defect prediction with association mining to discover defect pattern
Information and Software Technology
Software product integration: A case study-based synthesis of reference models
Information and Software Technology
Predicting faults using the complexity of code changes
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Proceedings of the 2006 international conference on Empirical software engineering issues: critical assessment and future directions
Usage of multiple prediction models based on defect categories
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Studying the fix-time for bugs in large open source projects
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
High-impact defects: a study of breakage and surprise defects
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Towards adopting ODC in automation application development projects
Proceedings of the 5th India Software Engineering Conference
Software defect analysis of a multi-release telecommunications system
PROFES'05 Proceedings of the 6th international conference on Product Focused Software Process Improvement
Applying DPPI: a defect causal analysis approach using bayesian networks
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
Using process mining metrics to measure noisy process fidelity
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
Controversy Corner: On the relationship between comment update practices and Software Bugs
Journal of Systems and Software
Controversy Corner: Preserving knowledge in software projects
Journal of Systems and Software
An industrial study on the risk of software changes
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Journal of Systems and Software
Bug prediction using entropy-based measures
International Journal of Knowledge Engineering and Data Mining
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There are three interdependent factors that drive our development processes: interval, quality and cost. As market pressures continue to demand new features ever more rapidly, the challenge is to meet those demands while increasing, or at least not sacrificing, quality. One advantage of defect prevention as an upstream quality improvement practice is the beneficial effect it can have on interval: higher quality early in the process results in fewer defects to be found and repaired in the later parts of the process, thus causing an indirect interval reduction.We report a retrospective analysis of the defect modification requests (MRs) discovered while building, testing, and deploying a release of a network element as part of an optical transmission network. The study consists of three investigations: a root-cause defect analysis (RCA) study, a process metric study, and a code complexity investigation. Differing in the quantities that we anticipate to be related to found defects, they all have in common the goal of identifying early quality indicators.The core of this threefold study is the root-cause analysis. We present the experimental design of this case study in some detail and its integration into the development process. We discuss the novel approach we have taken to defect and root cause classification and the mechanisms we have used for randomly selecting the MRs, to analyze and collecting the analyses via a web interface. We present the results of our analyses of the MRs and describe the defects and root causes that we found, and delineate the countermeasures created either to prevent those defects and their root causes or to detect them at the earliest possible point in the development process. We conclude the report on the root-cause analysis with lessons learned from the case study and from our experiences during subsequent usage of this analysis methodology for in-process measurement.Beyond the root-cause analysis, we first present our findings on the correlation between defects detected and the adherence to our development process. Second, we report on our experience with analyzing static code properties and their relation to observed defect numbers and defect densities.