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Inforence: effective fault localization based on information-theoretic analysis and statistical causal inference |
Farid FEYZI, Saeed PARSA() |
Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran |
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Abstract In this paper, a novel approach, Inforence, is proposed to isolate the suspicious codes that likely contain faults. Inforence employs a feature selection method, based on mutual information, to identify those bug-related statements that may cause the program to fail. Because the majority of a program faults may be revealed as undesired joint effect of the program statements on each other and on program termination state, unlike the state-of-the-art methods, Inforence tries to identify and select groups of interdependent statements which altogether may affect the program failure. The interdependence amongst the statements is measured according to their mutual effect on each other and on the program termination state. To provide the context of failure, the selected bug-related statements are chained to each other, considering the program static structure. Eventually, the resultant causeeffect chains are ranked according to their combined causal effect on program failure. To validate Inforence, the results of our experimentswith seven sets of programs include Siemens suite, gzip, grep, sed, space, make and bash are presented. The experimental results are then compared with those provided by different fault localization techniques for the both single-fault and multi-fault programs. The experimental results prove the outperformance of the proposed method compared to the state-of-the-art techniques.
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Keywords
fault localization
debugging
backward dynamic slice
mutual information
feature selection
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Corresponding Author(s):
Saeed PARSA
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Just Accepted Date: 07 July 2017
Online First Date: 04 September 2018
Issue Date: 29 May 2019
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1 |
SParsa, M Vahidi-Asl, MAsadi-Aghbolaghi. Hierarchy-Debug: a scalable statistical technique for fault localization. Software Quality Journal, 2014, 22(3): 427–466
https://doi.org/10.1007/s11219-013-9199-x
|
2 |
W EWong, VDebroy, RGao, Y Li. The DStar method for effective software fault localization. IEEE Transactions on Reliability, 2014, 63(1): 290–308
https://doi.org/10.1109/TR.2013.2285319
|
3 |
W EWong, VDebroy, DXu. Towards better fault localization: a crosstab-based statistical approach. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(3): 378–396
https://doi.org/10.1109/TSMCC.2011.2118751
|
4 |
W EWong, VDebroy, BChoi. A family of code coverage-based heuristics for effective fault localization. Journal of Systems and Software, 2010, 83(2): 188–208
https://doi.org/10.1016/j.jss.2009.09.037
|
5 |
W EWong, YQi. BP neural network-based effective fault localization. International Journal of Software Engineering and Knowledge Engineering, 2009, 19(4): 573–597
https://doi.org/10.1142/S021819400900426X
|
6 |
W EWong, VDebroy, RGolden, X Xu, BThuraisingham. Effective software fault localization using an RBF neural network. IEEE Transactions on Reliability, 2012, 61(1): 149–169
https://doi.org/10.1109/TR.2011.2172031
|
7 |
RAbreu, P Zoeteweij, A JVan Gemund. An evaluation of similarity coefficients for software fault localization. In: Proceedings of the International Symposium on Dependable Computing. 2006, 39–46
https://doi.org/10.1109/PRDC.2006.18
|
8 |
LNaish, HLee, KRamamohanarao. A model for spectra-based software diagnosis. ACM Transactions on Software Engineering and Methodology (TOSEM), 2011, 20(3): 11
https://doi.org/10.1145/2000791.2000795
|
9 |
SRoychowdhury, S Khurshid. Software fault localization using feature selection. In: Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering. 2011, 11–18
https://doi.org/10.1145/2070821.2070823
|
10 |
SRoychowdhury, S Khurshid. A family of generalized entropies and its application to software fault localization. In: Proceedings of the 6th IEEE International Conference on Intelligent Systems. 2012, 368–373
https://doi.org/10.1109/IS.2012.6335163
|
11 |
LJiang, ZSu. Context-aware statistical debugging: from bug predictors to faulty control flow paths. In: Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering. 2007, 184–193
https://doi.org/10.1145/1321631.1321660
|
12 |
HLiu, JSun, LLiu, H Zhang. Feature selection with dynamic mutual information. Pattern Recognition, 2009, 42(7): 1330–1339
https://doi.org/10.1016/j.patcog.2008.10.028
|
13 |
XSun, YLiu, MXu, HChen, JHan, K Wang. Feature selection using dynamic weights for classification. Knowledge-Based Systems, 2013, 37: 541–549
https://doi.org/10.1016/j.knosys.2012.10.001
|
14 |
P AEstévez, M Tesmer, C APerez , J MZurada. Normalized mutual information feature selection. IEEE Transactions on Neural Networks, 2009, 20(2): 189–201
https://doi.org/10.1109/TNN.2008.2005601
|
15 |
G KBaah, A Podgurski, M JHarrold. Causal inference for statistical fault localization. In: Proceedings of the 19th International Symposium on Software Testing and Analysis. 2010, 73–84
https://doi.org/10.1145/1831708.1831717
|
16 |
G KBaah, A Podgurski, M JHarrold. Mitigating the confounding effects of program dependences for effective fault localization. In: Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering. 2011, 146–156
https://doi.org/10.1145/2025113.2025136
|
17 |
G KBaah, A Podgurski, M JHarrold. Matching Test Cases for Effective Fault Localization. Georgia Institute of Technology, 2011
|
18 |
GShu. Statistical estimation of software reliability and failure-causing effect. Doctoral Dissertation, Case Western Reserve University, 2014
|
19 |
MWeiser. Program slicing. In: Proceedings of the 5th International Conference on Software Engineering. 1981, 439–449
|
20 |
TDenmat, M Ducassé, ORidoux. Data mining and cross-checking of execution traces: a re-interpretation of Jones, Harrold and Stasko test information. In: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering. 2005, 396–399
https://doi.org/10.1145/1101908.1101979
|
21 |
S FSun, A Podgurski. Properties of effective metrics for coveragebased statistical fault localization. In: Proceedings of the IEEE International Conference on Software Testing, Verification and Validation (ICST). 2016, 124–134
|
22 |
ZBai, GShu, APodgurski. NUMFL: localizing faults in numerical software using a value-based causal model. In: Proceedings of the 8th IEEE International Conference on Software Testing, Verification and Validation (ICST). 2015, 1–10
https://doi.org/10.1109/ICST.2015.7102597
|
23 |
ZBai, GShu, APodgurski. Causal inference based fault localization for numerical software with NUMFL. Software Testing, Verification and Reliability, 2017, 27(6): e1613
https://doi.org/10.1002/stvr.1613
|
24 |
RGore, P F Reynolds. Reducing confounding bias in predicate-level statistical debugging metrics. In: Proceedings of the 34th International Conference on Software Engineering. 2012, 463–473
https://doi.org/10.1109/ICSE.2012.6227169
|
25 |
XWang, S CCheung, W KChan, Z Zhang. Taming coincidental correctness: coverage refinement with context patterns to improve fault localization. In: Proceedings of the 31st International Conference on Software Engineering. 2009, 45–55
https://doi.org/10.1109/ICSE.2009.5070507
|
26 |
WMasri, R AAssi. Prevalence of coincidental correctness and mitigation of its impact on fault localization. ACM Transactions on Software Engineering and Methodology (TOSEM), 2014, 23(1): 8
https://doi.org/10.1145/2559932
|
27 |
YMiao, ZChen, SLi, ZZhao, YZhou. Identifying coincidental correctness for fault localization by clustering test cases. SEKE. 2012, 267–272
|
28 |
XZhang, NGupta, RGupta. Locating faulty code by multiple points slicing. Software: Practice and Experience, 2007, 37(9): 935–961
https://doi.org/10.1002/spe.795
|
29 |
XZhang, NGupta, RGupta. Pruning dynamic slices with confidence. ACM SIGPLAN Notices, 2006, 41(6): 169–180
https://doi.org/10.1145/1133255.1134002
|
30 |
T MCover, J AThomas. Elements of Information Theory. Hoboken: John Wiley & Sons, 2012
|
31 |
JBurbea, C RRao. On the convexity of some divergence measures based on entropy functions. IEEE Transactions on Information Theory, 1982, 28: 489–495
https://doi.org/10.1109/TIT.1982.1056497
|
32 |
T D BLe, DLo, MLi. Constrained feature selection for localizing faults. In: Proceedings of the International Conference on Software Maintenance and Evolution. 2015, 501–505
https://doi.org/10.1109/ICSM.2015.7332502
|
33 |
JXu, ZZhang, W KChan, T H Tse, SLi. A general noise-reduction framework for fault localization of Java programs. Information and Software Technology, 2013, 55(5): 880–896
https://doi.org/10.1016/j.infsof.2012.08.006
|
34 |
AZeller, R Hildebrandt. Simplifying and isolating failure-inducing input. IEEE Transactions on Software Engineering, 2002, 28(2): 183–200
https://doi.org/10.1109/32.988498
|
35 |
AZeller. Isolating cause-effect chains from computer programs. In: Proceedings of the 10th ACM SIGSOFT Symposium on Foundations of Software Engineering. 2002, 1–10
https://doi.org/10.1145/587051.587053
|
36 |
JPearl. Causality: models, reasoning and inference. Econometric Theory, 2003, 19: 675–685
|
37 |
JFerrante, K J Ottenstein, J DWarren. The program dependence graph and its use in optimization. ACM Transactions on Programming Languages and Systems (TOPLAS), 1987, 9(3): 319–349
https://doi.org/10.1145/24039.24041
|
38 |
APodgurski, L AClarke. A formal model of program dependences and its implications for software testing, debugging, and maintenance. IEEE Transactions on Software Engineering, 1990, 16(9): 965–979
https://doi.org/10.1109/32.58784
|
39 |
GShu, BSun, APodgurski, F Cao. MFL: method-level fault localization with causal inference. In: Proceedings of the 6th IEEE International Conference on Software Testing, Verification and Validation. 2013, 124–133
https://doi.org/10.1109/ICST.2013.31
|
40 |
P CAustin. Statistical criteria for selecting the optimal number of untreated subjects matched to each treated subject when using many-toone matching on the propensity score. American Journal of Epidemiology, 2010, 172(9): 1092–1097
https://doi.org/10.1093/aje/kwq224
|
41 |
D WHosmer, S Lemeshow. Applied Logistic Regression. Hoboken: John Wiley & Sons, 2013
https://doi.org/10.1002/9781118548387
|
42 |
B BHansen, S O Klopfer. Optimal full matching and related designs via network flows. Journal of Computational and Graphical Statistics, 2006, 15(3): 609–627
https://doi.org/10.1198/106186006X137047
|
43 |
JFriedman, THastie, RTibshirani. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 2010, 33(1): 1–22
https://doi.org/10.18637/jss.v033.i01
|
44 |
DHo, KImai, GKing, E A Stuart. MatchIt: nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 2011, 42(8): 1–28
https://doi.org/10.18637/jss.v042.i08
|
45 |
MHutchins, HFoster, TGoradia, T Ostrand. Experiments on the effectiveness of dataflow-and control-flow-based test adequacy criteria. In: Proceedings of the 16th International Conference on Software Engineering. 1994, 191–200
https://doi.org/10.1109/ICSE.1994.296778
|
46 |
XXie, F CKuo, T YChen, S Yoo, MHarman. Provably optimal and human-competitive results in SBSE for spectrum based fault localisation. In: Proceedings of International Symposium on Search Based Software Engineering, Springer Berlin Heidelberg. 2013, 224–238
https://doi.org/10.1007/978-3-642-39742-4_17
|
47 |
YYu , J AJones, M JHarrold. An empirical study of the effects of test-suite reduction on fault localization. In: Proceedings of the 30th International Conference on Software Engineering. 2008, 201–210
https://doi.org/10.1145/1368088.1368116
|
48 |
I HWitten, EFrank. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann, 2005
|
49 |
L CAscari, L YAraki, A RPozo, S R Vergilio. Exploring machine learning techniques for fault localization. In: Proceedings of the 10th Latin American Test Workshop. 2009, 1–6
https://doi.org/10.1109/LATW.2009.4813783
|
50 |
DLo, LJiang, ABudi. Comprehensive evaluation of association measures for fault localization. In: Proceedings of IEEE International Conference on Software Maintenance. 2010, 1–10
|
51 |
J AJones, M J Harrold. Empirical evaluation of the tarantula automatic fault-localization technique. In: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering. 2005, 273–282
https://doi.org/10.1145/1101908.1101949
|
52 |
SRoychowdhury. A mixed approach to spectrum-based fault localization using information theoretic foundations. Doctoral Dissertation, the University of Texas at Austin, 2013
|
53 |
BJiang, ZZhang, T HTse, T Y Chen. How well do test case prioritization techniques support statistical fault localization. In: Proceedings of the 33rd Annual IEEE International Computer Software and Applications Conference. 2009, 99–106
https://doi.org/10.1109/COMPSAC.2009.23
|
54 |
BJiang, ZZhang, W KChan, T H Tse. Adaptive random test case prioritization. In: Proceedings of the 24th IEEE/ACM International Conference on Automated Software Engineering. 2009, 233–244
https://doi.org/10.1109/ASE.2009.77
|
55 |
SYoo, MHarman, DClark. Fault localization prioritization: comparing information-theoretic and coverage-based approaches. ACM Transactions on Software Engineering and Methodology (TOSEM), 2013, 22(3): 19
https://doi.org/10.1145/2491509.2491513
|
56 |
SRoychowdhury, S Khurshid. A novel framework for locating software faults using latent divergences. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2011, 49–64
https://doi.org/10.1007/978-3-642-23808-6_4
|
57 |
RAbreu, P Zoeteweij, RGolsteijn, A JVan Gemund. A practical evaluation of spectrum-based fault localization. Journal of Systems and Software, 2009, 82(11): 1780–1792
https://doi.org/10.1016/j.jss.2009.06.035
|
58 |
SYoo. Evolving human competitive spectra-based fault localisation techniques. In: Proceedings of International Symposium on Search Based Software Engineering. 2012, 244–258
https://doi.org/10.1007/978-3-642-33119-0_18
|
59 |
W EWong, RGao, YLi, RAbreu, FWotawa. A survey on software fault localization. IEEE Transactions on Software Engineering, 2016, 42(8): 707–740
https://doi.org/10.1109/TSE.2016.2521368
|
60 |
T D BLe, FThung, DLo. Theory and practice, do they matchfi a case with spectrum-based fault localization. In: Proceedings of the 29th IEEE International Conference on Software Maintenance (ICSM). 2013, 380–383
|
61 |
SYoo, XXie, F CKuo, T Y Chen, MHarman. No pot of gold at the end of program spectrum rainbow: greatest risk evaluation formula does not exist. Research Note (RN), 2014, 14(14): 14
|
62 |
XJu, SJiang, XChen, X Wang, YZhang, HCao. HSFal: effective fault localization using hybrid spectrum of full slices and execution slices. Journal of Systems and Software, 2014, 90: 3–17
https://doi.org/10.1016/j.jss.2013.11.1109
|
63 |
NGupta , HHe, XZhang, R Gupta. Locating faulty code using failureinducing chops. In: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering. 2005, 263–272
https://doi.org/10.1145/1101908.1101948
|
64 |
XZhang, NGupta, RGupta. Locating faults through automated predicate switching. In: Proceedings of the 28th International Conference on Software Engineering. 2006, 272–281
https://doi.org/10.1145/1134285.1134324
|
65 |
DJeffrey, NGupta, RGupta. Fault localization using value replacement. In: Proceedings of the International Symposium on Software Testing and Analysis. 2008, 167–178
https://doi.org/10.1145/1390630.1390652
|
66 |
XZhang, STallam, NGupta, R Gupta. Towards locating execution omission errors. ACM Sigplan Notices, 2007, 42(6): 415–424
https://doi.org/10.1145/1273442.1250782
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