Locating causes of program failures

Which is the defect that causes a software failure? By comparing the program states of a failing and a passing run, we can identify the state differences that cause the failure. However, these state differences can occur all over the program run. Therefore, we focus in space on those variables and values that are relevant for the failure, and in time on those moments where cause transitions occur - moments where new relevant variables begin being failure causes: "Initially, variable argc was 3; therefore, at shell-sort(), variable a[2] was 0, and therefore, the program failed." In our evaluation, cause transitions locate the failure-inducing defect twice as well as the best methods known so far.

[1]  Andreas Zeller,et al.  Why Programs Fail: A Guide to Systematic Debugging , 2005 .

[2]  Alex Groce,et al.  Error explanation with distance metrics , 2004, International Journal on Software Tools for Technology Transfer.

[3]  Shriram Krishnamurthi,et al.  Automated Fault Localization Using Potential Invariants , 2003, ArXiv.

[4]  Shriram Krishnamurthi,et al.  Automated Fault Localization Using Potential Invariants , 2003, ArXiv.

[5]  Steven P. Reiss,et al.  Fault localization with nearest neighbor queries , 2003, 18th IEEE International Conference on Automated Software Engineering, 2003. Proceedings..

[6]  Michael I. Jordan,et al.  Bug isolation via remote program sampling , 2003, PLDI.

[7]  James A. Jones,et al.  Visualization of test information to assist fault localization , 2002, Proceedings of the 24th International Conference on Software Engineering. ICSE 2002.

[8]  Andreas Zeller,et al.  Simplifying and Isolating Failure-Inducing Input , 2002, IEEE Trans. Software Eng..

[9]  A. Zeller.  Isolating cause-effect chains from computer programs , 2002, SIGSOFT '02/FSE-10.

[10]  Andreas Zeller,et al.  Visualizing Memory Graphs , 2001, Software Visualization.

[11]  Gregg Rothermel,et al.  An empirical investigation of the relationship between spectra differences and regression faults , 2000, Softw. Test. Verification Reliab..

[12]  William G. Griswold,et al.  Dynamically discovering likely program invariants to support program evolution , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[13]  Gregg Rothermel,et al.  Empirical Studies of a Safe Regression Test Selection Technique , 1998, IEEE Trans. Software Eng..

[14]  Thomas J. Ostrand,et al.  Experiments on the effectiveness of dataflow- and control-flow-based test adequacy criteria , 1994, Proceedings of 16th International Conference on Software Engineering.

[15]  Jeffrey M. Voas,et al.  PIE: A Dynamic Failure-Based Technique , 1992, IEEE Trans. Software Eng..

[16]  Thomas W. Reps,et al.  The use of program dependence graphs in software engineering , 1992, International Conference on Software Engineering.

[17]  Janusz W. Laski,et al.  Dynamic slicing of computer programs , 1990, J. Syst. Softw..

[18]  Joseph Robert Horgan,et al.  Dynamic program slicing , 1990, PLDI '90.

[19]  Mark Weiser,et al.  Programmers use slices when debugging , 1982, CACM.