Automated program repair(APR) is in the spotlight in academia and the field to reduce the time and cost of maintenance for developers. Recently, APR has continued to study based on deep-learning models to understand and learn how to fix software bugs. Text-to-Text Transfer Transformer(T5), which scored state-of-the-art in natural language processing benchmarks, also showed promising results on program repair in recent studies. In deep-learning-based program repair studies, studies commonly propose code abstraction techniques to avoid vocabulary problems and learn fine code transformation to generate bug-fixing patches. However, there is not enough systematic analysis of code abstraction according to each bug type in deep-learning-based program repair. Therefore, We leverage TFix, T5-based program repair, to evaluate how code abstraction techniques affect neural program repair. Our experimental results showed that defect-specific code abstraction achives a higher average BLEU score than the existing code abstraction technique in both T5 and multilingual-T5(mT5) model-based TFix results. Also, mT5 model-based TFix, which is applied defect-specific code abstraction, gets a higher BLEU score in 37 error types of 52 ESLint error types than TFix.

Template-based automatic program repair (T-APR) techniques depend on the quality of bug-fixing templates. For such templates to be of sufficient quality for T-APR techniques to succeed, they must satisfy three criteria: applicability, fixability, and efficiency. Existing template mining approaches select templates based only on the first criteria, and are thus suboptimal in their performance. This study proposes a multi-objective optimization-based bug-fixing template mining method for T-APR in which we estimate template quality based on nine code abstraction tasks and three objective functions. Our method determines the optimal code abstraction strategy (i.e., the optimal combination of abstraction tasks) which maximizes the values of three objective functions and generates a final set of bug-fixing templates by clustering template candidates to which the optimal abstraction strategy is applied. Our preliminary experiment demonstrated that our optimized strategy can improve templates’ applicability and efficiency by 7% and 146% over the existing mining technique, respectively. We therefore conclude that the multi-objective optimization-based template mining technique effectively finds high-quality bug-fixing templates.

Power of scan operation is dominant factor. This paper proposed the structure to reduce scan power totally. The total scan power reduction architecture uses a duplicated transition monitoring window and sub-scan chains. Experimental results show 60% transition reduction, 2-4% fault coverage improvement, and 25% scan-in and 26% scan-out transition by the TOSCA