Debugging of Java programs using a model-based approach

Automated and Algorithmic Debugging, 2000

Lecture Notes in Computer Science, 2002

We describe the extension of the well-known model-based diagnosis approach to the location of errors in imperative programs (exhibited on a subset of the Java language). The source program is automatically converted to a logical representation (called model). Given this model and a particular test case or set of test cases, a program-independent search algorithm determines a the minimal sets of statements whose incorrectness can explain incorrect outcomes when the program is executed on the test cases, and which can then be indicated to the developer by the system. We analyze example cases and discuss empirical results from a Java debugger implementation incorporating our approach. The use of AI techniques is more flexible than traditional debugging techniques such as algorithmic debugging and program slicing.

Arxiv preprint cs/0011027, 2000

ECAI, 2000

This paper extends our previous work on the representation and analysis of Java programs for diagnosis in a new direction by providing a description and analysis of the issues arising from handling object references in dependency-based models of Java programs. We empirically compare dependencybased models with a value-based model using a set of example programs in terms of required user interaction (questions put to the user) and examine and incorporate specific interesting error categories. Apart from being based on experience with an actual implementation of the various models, the model extensions and analysis deal with aliasing, an issue that the programming language community has been examining for a long time, and that is also crucial to object-orientedness.

Intelligent Problem Solving. …, 2000

2007

Program debugging is a difficult and time-consuming task. Our ultimate goal in this work is to help developers reduce the space of potential root causes for failures, which can, in turn, improve the turn around time for bug fixes. We propose a novel and very different approach. Rather then focusing on how a program behaves by analyzing its source code and/or execution traces, we concentrate on how it should behave with respect to a given behavioral model. We identify and verify slices of the behavior model, that, once implemented wrong in the program, can potentially lead to failures. Not only do we identify functional differences between the program and its model, but we also provide a ranked list of diagnoses which might explain (or be associated with) these differences. Our experiments suggest that the proposed approach can be quite effective in reducing the search space for potential root causes for failures.

ECAI, 2002

Lecture Notes in Computer Science, 2002

Even with modern software development methodologies, the actual debugging of source code, i.e., location and identification of errors in the program when errant behavior is encountered during testing, remains a crucial part of software development. To apply model-based diagnosis techniques, which have long been state of the art in hardware diagnosis, for automatic debugging, a model of a given program must be automatically created from the source code. This work describes a model that reflects the execution semantics of the Java language, including exceptions and unstructured control flow, thereby providing unprecedented scope in the application of model-based diagnosis to programs. Besides the structural model building process, a behavioral description of some of the model components is given. Finally, impacts of the modeling decisions on the diagnostic process are considered.

2008 23rd IEEE/ACM International Conference on Automated Software Engineering, 2008

Developing model-based automatic debugging strategies has been an active research area for several years, with the aim of locating defects in a program by utilising fully automated generation of a model of the program from its source code. We provide an overview of current techniques in model-based debugging and assess strengths and weaknesses of the individual approaches. An empirical comparison is presented that investigates the relative accuracy of different models on a set of test programs and fault assumptions, showing that our abstract interpretation based model provides high accuracy at significantly less computational effort than slightly more accurate techniques. We compare a range of model-based debugging techniques with other state-of-the-art automated debugging approaches and outline possible future developments in automatic debugging using model-based reasoning as the central unifying component in a comprehensive framework.