Optimal Reordering Trace Files for Improving Software Testing Suitcase
Source abstract
Abstract
An invariant can be described as an essential relationship between program variables. The invariants are very useful in software checking and verification. The tools that are used to detect invariants are invariant detectors. There are two types of invariant detectors: dynamic invariant detectors and static invariant detectors. Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm. Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program; then, it gathers the values of its variables, and finally, it detects relationships between the variables based on a simple statistical analysis. This method has some drawbacks. One of its biggest drawbacks is its overwhelming time order. It is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file. A mechanism is proposed in order to reduce differences in adjacent trace files. It is done by applying some special techniques of mutation/crossover in genetic algorithm (GA). An experiment is run to assess the benefits of this approach. Experimental findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Software Testing and Debugging Techniques · Anomaly Detection Techniques and Applications · Machine Learning and Data Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (51)
Show source-provided references
1. A fuzzy logic based framework for assessing the maturity level of Indian small scale software organizations
2. Mining temporal invariants from partially ordered logs10.1145/2094091.2094101 ↗
3. Automatic generation of program specifications
4. Automated support for program refactoring using invariants
5. Experimental program analysis10.1016/j.infsof.2009.10.002 ↗
6. An ensemble of locally reliable cluster solutions10.3390/app10051891 ↗
7. On comparing, classifying and clustering several dependent regression models10.1080/00949655.2019.1615489 ↗
8. Diagnosis and clustering of power transformer winding fault types by cross-correlation and clustering analysis of FRA results10.1049/iet-gtd.2018.5812 ↗
9. Fabricated and Shia Malay translated hadith as negative fuzzy logic ranking indicator on Malay information retrieval
10. A fuzzy-based buffer split algorithm for buffer attack detection in internet of things
11. Analysis of adaptive perturb and observe-fuzzy logic control maximum power point tracking for photovoltaic boost DC–DC converter10.30534/ijatcse/2019/3181.62019 ↗
12. Fuzzy parameterized dual hesitant fuzzy soft sets and its application in TOPSIS10.13189/ms.2020.080104 ↗
13. Hybrid fuzzy-disturbance observer for estimating disturbance in styrene polymerization process10.1088/1757-899X/778/1/012089 ↗
14. Integrated machine learning methods with resampling algorithms for flood susceptibility prediction10.1016/j.scitotenv.2019.135983 ↗
15. Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain10.1016/j.scitotenv.2019.134474 ↗
16. Earth fissure hazard prediction using machine learning models10.1016/j.envres.2019.108770 ↗
17. A deep learning ensemble approach for diabetic retinopathy detection10.1109/ACCESS.2019.2947484 ↗
18. Snow avalanche hazard prediction using machine learning methods10.1016/j.jhydrol.2019.123929 ↗
19. Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters10.1080/19942060.2018.1553742 ↗
20. Neuroevolution-based autonomous robot navigation: A comparative study10.1016/j.cogsys.2020.04.001 ↗
21. Asymmetric heavy-tailed vector auto-regressive processes with application to financial data10.1080/00949655.2019.1680675 ↗
22. On the asymptotic distribution of the periodograms for the discrete time harmonizable simple processes10.1007/s11203-018-9189-5 ↗
23. Chebyshev cardinal wavelets for nonlinear stochastic differential equations driven with variable-order fractional Brownian motion10.1016/j.chaos.2019.04.040 ↗
24. Robust mixture modeling based on two-piece scale mixtures of normal family10.3390/axioms8020038 ↗
25. Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model10.1016/j.scitotenv.2019.01.204 ↗
26. On the asymptotic distribution for the periodograms of almost periodically correlated (cyclostationary) processes10.1016/j.dsp.2018.07.004 ↗
27. Two-piece location-scale distributions based on scale mixtures of normal family10.1080/03610926.2017.1295160 ↗
28. Periodically correlated modeling by means of the periodograms asymptotic distributions10.1007/s00362-016-0748-9 ↗
29. A Bayesian approach to robust skewed autoregressive processes10.1177/0008068317732196 ↗
30. Testing the difference between two independent regression models10.1080/03610926.2014.960584 ↗
31. On comparing and classifying several independent linear and non-linear regression models with symmetric errors10.3390/sym11060820 ↗
32. A new method to compare the spectral densities of two independent periodically correlated time series10.1016/j.matcom.2018.12.008 ↗
33. Testing the difference between spectral densities of two independent periodically correlated (cyclostationary) time series models10.1080/03610926.2018.1472776 ↗
34. On comparing two dependent linear and nonlinear regression models10.1520/JTE20170461 ↗
35. Testing the equality of two independent regression models10.1080/03610926.2017.1343847 ↗
36. KGSA: A gravitational search algorithm for multimodal optimization based on k-means niching technique and a novel elitism strategy10.1515/math-2018-0132 ↗
37. An operational matrix method for nonlinear variable-order time fractional reaction-diffusion equation involving Mittag-Leffler kernel10.1140/epjp/s13360-020-00158-5 ↗
38. A tool for using specification to check code
39. H. Schmitt and B. Weiß, “Inferring invariants by static analysis in KeY,” University of Karlsruhe, 2007.
40. The daikon system for dynamic detection of likely invariants10.1016/j.scico.2007.01.015 ↗
41. Data flow analysis for verifying properties of concurrent programs10.1145/1040291.1040292 ↗
42. Discovering models of software processes from event-based data10.1145/287000.287001 ↗
43. Event-based detection of concurrency10.1145/291252.288214 ↗
44. Query-based debugging of object-oriented programs
45. Efficient incremental algorithms for dynamic detection of likely invariants
46. Model checking merged program traces10.1016/j.entcs.2009.05.047 ↗
47. Harnessing a refinement theory to compute loop functions10.1016/j.entcs.2009.07.010 ↗
48. Specification and runtime verification of java card programs10.1016/j.entcs.2009.05.045 ↗
49. Dynamically discovering program invariants involving collections
50. Mitchell · An Introduction to Genetic Algorithms · 199810.7551/mitpress/3927.001.0001 ↗
51. Generalized cycle crossover for graph partitioning