Abstract
Fault localization is one of the main tasks of software debugging. Developers spend a lot of time, cost, and effort to locate the faults correctly manually. For reducing this effort, many automatic fault localization techniques have been proposed, which inputs test suites and outputs a sorted list of faulty entities of the program. For further enhancement in this area, we developed a system called SILearning, which is based on invariant analysis. It learns from some existing fixed bugs to locate faulty methods in the program. It combines machine-learned ranking, program invariant differences, and spectrum-based fault localization (SBFL). Using the execution of test cases and code coverage analysis, it obtains each method’s invariant differences and the suspiciousness value based on the program spectral location and uses them as features for ranking the faulty methods. The experimental analysis of SILearning has been performed on the dataset of real fault which is extracted from the database Defects4J. The tools used in this research are Daikon and cobertura for detection of the invariants and code coverage, respectively. The results show that SILearning performs better when combined features are utilized and can successfully locate the faulty methods on average for 76.1, 90.4, 108.2, 123, and 143.5 at the top positions of 1, 2, 3, 4, and 5.
Highlights
The software has turned into a consistent need in our daily lives by providing a variety of services such as word processing, ATM, mobile applications, navigation systems, etc
Using the execution of test cases and code coverage analysis, it obtains each method’s invariant differences and the suspiciousness value based on the program spectral location and uses them as features for ranking the faulty methods
We propose a system called SILearning, which uses a technique of machine learning called learning-to-rank that recognizes those methods which are faulty and ranks them based on their suspiciousness. Doing this examines three kinds of features: code coverage analysis, a score of suspiciousness which is calculated by spectrum-based fault localization (SBFL), and the difference of subset of likely and dynamic invariants that are examined by running on both test cases
Summary
The software has turned into a consistent need in our daily lives by providing a variety of services such as word processing, ATM, mobile applications, navigation systems, etc. Even though these strategies have been demonstrated successfully in numerous specific situations, but there is a need to be more enhanced in the efficiency of localizing different faults To improve this direction, we propose a system called SILearning, which uses a technique of machine learning called learning-to-rank that recognizes those methods which are faulty and ranks them based on their suspiciousness. We propose a system called SILearning, which uses a technique of machine learning called learning-to-rank that recognizes those methods which are faulty and ranks them based on their suspiciousness Doing this examines three kinds of features: code coverage analysis, a score of suspiciousness which is calculated by SBFL, and the difference of subset of likely and dynamic invariants that are examined by running on both test cases (failing and passing).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
