International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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OAPR-HOML'1: Optimal automated program repair approach based on hybrid improved grasshopper optimization and opposition learning based artificial neural network

  • MAMATHA, T. (Dept. of CSE, JNTUA University) ;
  • RAMA SUBBA REDDY, B. (Dept. of CSE, SV College of Engineering) ;
  • BINDU, C SHOBA (Dept. of CSE, JNTUA University)
  • Received : 2022.04.05
  • Published : 2022.04.30
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Abstract

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

Keywords

Acknowledgement

Authors thank, K. Abijith Rao (CEO, SNIST) and Management of Sreenidhi Institute of Science and Technology, JNT University Anantapuramu for providing a assistance to establish working environment in the lab to carry out my present research. Authors also acknowledge, Prof. C V Tomy ( Director), T. Shiva Reddy(Prinicipal), Dr. Aruna varanasi (head of the department) for continuous moral support, help and encouragement.

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