Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Experiments on slicing-based debugging aids
Papers presented at the first workshop on empirical studies of programmers on Empirical studies of programmers
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A knowledge-based system for MVS dump analysis
IBM Systems Journal
Incorporating Probabilistic Reasoning in a Reactive Program Debugging System
IEEE Expert: Intelligent Systems and Their Applications
Graphical models for problem solving
Computing in Science and Engineering
On the relationship between model-based debugging and program slicing
Artificial Intelligence
Debugging Hardware Designs Using a Value-Based Model
Applied Intelligence
Computer
A Distributed Learning Algorithm for Bayesian Inference Networks
IEEE Transactions on Knowledge and Data Engineering
Using Design Information to Identify Structural Software Faults
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Using Multiple Models for Debugging VHDL Designs
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Comparing Two Models for Software Debugging
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Debugging VHDL Designs: Introducing Multiple Models and First Empirical Results
Applied Intelligence
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
A survey of intelligent debugging
AI Communications
The probabilistic program dependence graph and its application to fault diagnosis
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
Neural Networks
Cooperative bug isolation: winning thesis of the 2005 ACM doctoral dissertation competition
Cooperative bug isolation: winning thesis of the 2005 ACM doctoral dissertation competition
Argus: online statistical bug detection
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
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Software errors abound in the world of computing. Sophisticated computer programs rank high on the list of the most complex systems ever created by humankind. The complexity of a program or a set of interacting programs makes it extremely difficult to perform offline verification of run-time behavior. Thus, the creation and maintenance of program code is often linked to a process of incremental refinement and ongoing detection and correction of errors. To be sure, the detection and repair of program errors is an inescapable part of the process of software development. However, run-time software errors may be discovered in fielded applications days, months, or even years after the software was last modified—especially in applications composed of a plethora of separate programs created and updated by different people at different times. In such complex applications, software errors are revealed through the run-time interaction of hundreds of distinct processes competing for limited memory and CPU resources. Software developers and support engineers responsible for correcting software problems face difficult challenges in tracking down the source of run-time errors in complex applications. The information made available to engineers about the nature of a failure often leaves open a wide range of possibilities that must be sifted through carefully in searching for an underlying error.