Mining Software Repository for Cleaning Bugs Using Data Mining Technique
Source abstract
Abstract
Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%. The pruning strategy was adopted based on evaluation measures. Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values. To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects. The evaluation showed that the results of our proposal are promising. Practitioners and developers can utilize these rules for defect prediction during early software development.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Software Engineering Research · Software Reliability and Analysis Research · Software System Performance and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (41)
Show source-provided references
1. Pruned associative classification technique for the medical image diagnosis system
2. CMAR: Accurate and efficient classification based on multiple class-association rules
3. Hybrid medical image classification using association rule mining with decision tree algorithm
4. Integrating classification and association rule mining
5. Mining quantitative association rules in large relational tables
6. A fuzzy classifier approach to estimating software quality10.1016/j.ins.2013.04.027 ↗
7. Software defect prediction using non-negative matrix factorization10.4304/jsw.6.11.2114-2120 ↗
8. A novel approach to rank association rules using genetic algorithm
9. Mining association rules between sets of items in large databases
10. Applications of association rule mining in different databases
11. Selecting the right interestingness measure for association patterns
12. What makes patterns interesting in knowledge discovery systems10.1109/69.553165 ↗
13. Mining efficient association rules through apriori algorithm using attributes and comparative analysis of various association rule algorithms
14. Improving efficiency of apriori algorithm using transaction reduction
15. Answering the most correlated n association rules efficiently
16. Mining frequent itemsets using genetic algorithm10.5121/ijaia.2010.1411 ↗
17. Software defect prediction using relational association rule mining10.1016/j.ins.2013.12.031 ↗
18. Generalized association rule mining with constraints10.1016/j.ins.2011.05.016 ↗
19. Classification using association rules: Weaknesses and enhancements
20. T. Menzies, B. Caglayan, E. Kocaguneli, J. Krall, F. Peters et al., “The promise repository of empirical software engineering data,” West Virginia University, Department of Computer Science, USA, 2012. [Online]. Available: http://promise.site.uottawa.ca/SERepository/.
21. B. Ma, K. Dejaeger, J. Vanthienen and B. Baesens, “Software defect prediction based on association rule classification,” Available SSRN 1785381, 2011. [Online]. Available: http://dx.doi.org/10.2139/ssrn.1785381.10.2139/ssrn.1785381 ↗
22. A hybrid faulty module prediction using association rule mining and logistic regression analysis
23. Searching for rules to detect defective modules: A subgroup discovery approach10.1016/j.ins.2011.01.039 ↗
24. Zhang · Applied Mechanics and Materials · 2014 · 687 · 2182
25. Empirical assessment of machine learning based software defect prediction techniques10.1142/S0218213008003947 ↗
26. Very simple classification rules perform well on most commonly used datasets10.1023/A:1022631118932 ↗
27. Applying mining schemes to software fault prediction: A proposed approach aimed at test cost reduction
28. Software defect detection with rocus10.1007/s11390-011-9439-0 ↗
29. Sample-based software defect prediction with active and semi-supervised learning10.1007/s10515-011-0092-1 ↗
30. Robust prediction of fault-proneness by random forests
31. A general software defect-proneness prediction framework10.1109/TSE.2010.90 ↗
32. Evaluating defect prediction approaches: A benchmark and an extensive comparison10.1007/s10664-011-9173-9 ↗
33. Further thoughts on precision
34. V. R. Basili, L. C. Briand and W. Melo, “A validation of object-oriented design metrics. College Park, MD, 20742 USA: University of Maryland, Dept. of Computer Science, Technical Report, 1995.
35. Object oriented software quality prediction using general regression neural networks10.1145/1022494.1022515 ↗
36. Early estimation of software quality using in-process testing metrics: A controlled case study10.1145/1082983.1083304 ↗
37. Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes10.1109/TSE.2007.1015 ↗
38. A public bug database of github projects and its application in bug prediction
39. SZZ revisited: Verifying when changes induce fixes
40. A new approach to find predictor of software fault using association rule mining
41. Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine10.1093/clinchem/39.4.561 ↗