891 research outputs found
MintHint: Automated Synthesis of Repair Hints
Being able to automatically repair programs is an extremely challenging task.
In this paper, we present MintHint, a novel technique for program repair that
is a departure from most of today's approaches. Instead of trying to fully
automate program repair, which is often an unachievable goal, MintHint performs
statistical correlation analysis to identify expressions that are likely to
occur in the repaired code and generates, using pattern-matching based
synthesis, repair hints from these expressions. Intuitively, these hints
suggest how to rectify a faulty statement and help developers find a complete,
actual repair. MintHint can address a variety of common faults, including
incorrect, spurious, and missing expressions.
We present a user study that shows that developers' productivity can improve
manyfold with the use of repair hints generated by MintHint -- compared to
having only traditional fault localization information. We also apply MintHint
to several faults of a widely used Unix utility program to further assess the
effectiveness of the approach. Our results show that MintHint performs well
even in situations where (1) the repair space searched does not contain the
exact repair, and (2) the operational specification obtained from the test
cases for repair is incomplete or even imprecise
Precise propagation of fault-failure correlations in program flow graphs
Statistical fault localization techniques find suspicious faulty program entities in programs by comparing passed and failed executions. Existing studies show that such techniques can be promising in locating program faults. However, coincidental correctness and execution crashes may make program entities indistinguishable in the execution spectra under study, or cause inaccurate counting, thus severely affecting the precision of existing fault localization techniques. In this paper, we propose a BlockRank technique, which calculates, contrasts, and propagates the mean edge profiles between passed and failed executions to alleviate the impact of coincidental correctness. To address the issue of execution crashes, Block-Rank identifies suspicious basic blocks by modeling how each basic block contributes to failures by apportioning their fault relevance to surrounding basic blocks in terms of the rate of successful transition observed from passed and failed executions. BlockRank is empirically shown to be more effective than nine representative techniques on four real-life medium-sized programs. © 2011 IEEE.published_or_final_versionProceedings of the 35th IEEE Annual International Computer Software and Applications Conference (COMPSAC 2011), Munich, Germany, 18-22 July 2011, p. 58-6
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