Bugs as deviant behavior: a general approach to inferring errors in systems code
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Isolating cause-effect chains from computer programs
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
Vacuity Detection in Temporal Model Checking
CHARME '99 Proceedings of the 10th IFIP WG 10.5 Advanced Research Working Conference on Correct Hardware Design and Verification Methods
Automated path generation for software fault localization
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Locating faults through automated predicate switching
Proceedings of the 28th international conference on Software engineering
Static error detection using semantic inconsistency inference
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Finding more null pointer bugs, but not too many
PASTE '07 Proceedings of the 7th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Reachability analysis for annotated code
Proceedings of the 2007 conference on Specification and verification of component-based systems: 6th Joint Meeting of the European Conference on Software Engineering and the ACM SIGSOFT Symposium on the Foundations of Software Engineering
Darwin: an approach for debugging evolving programs
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Refinement of Trace Abstraction
SAS '09 Proceedings of the 16th International Symposium on Static Analysis
FM '09 Proceedings of the 2nd World Congress on Formal Methods
Making the Most of BMC Counterexamples
Electronic Notes in Theoretical Computer Science (ENTCS)
On the notion of vacuous truth
LPAR'07 Proceedings of the 14th international conference on Logic for programming, artificial intelligence and reasoning
Formal Methods in System Design
Cause clue clauses: error localization using maximum satisfiability
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Bug-Assist: assisting fault localization in ANSI-C programs
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Lazy abstraction with interpolants
CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
Whale: an interpolation-based algorithm for inter-procedural verification
VMCAI'12 Proceedings of the 13th international conference on Verification, Model Checking, and Abstract Interpretation
VMCAI'12 Proceedings of the 13th international conference on Verification, Model Checking, and Abstract Interpretation
VSTTE'12 Proceedings of the 4th international conference on Verified Software: theories, tools, experiments
Detecting inconsistencies via universal reachability analysis
Proceedings of the 2012 International Symposium on Software Testing and Analysis
BugRedux: reproducing field failures for in-house debugging
Proceedings of the 34th International Conference on Software Engineering
A solver for reachability modulo theories
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
Joogie: infeasible code detection for java
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
Fundamenta Informaticae - Fundamentals of Software Engineering 2007: Selected Contributions
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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A code fragment is inconsistent if it is not part of any normally terminating execution. Examples of such inconsistencies include code that is unreachable, code that always fails due to a run-time error, and code that makes conflicting assumptions about the program state. In this paper, we consider the problem of automatically explaining inconsistent code. This problem is difficult because traditional fault localization techniques do not apply. Our solution relies on a novel algorithm that takes an infeasible code fragment as input and generates a so-called error invariant automaton. The error invariant automaton is an abstraction of the input code fragment that only mentions program statements and facts that are relevant for understanding the cause of the inconsistency. We conducted a preliminary usability study which demonstrated that error invariant automata can help programmers better understand inconsistencies in code taken from real-world programs. In particular, access to an error invariant automata tripled the speed at which programmers could diagnose the cause of a code inconsistency.