• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.129-137
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• 2020

Recently, software size and program code complexity have increased due to application to various fields of software. Accordingly, the existence of program bugs inevitably occurs, and the cost of software maintenance is increasing. In open source projects, developers spend a lot of debugging time when solving a bug report assigned. To solve this problem, in this paper, we apply SeqGAN algorithm to software bug repair. In detail, the SeqGAN model is trained based on the source code. Open similar source codes during the learning process are also used. To evaluate the suitability for the generated candidate patch, a fitness function is applied, and if all test cases are passed, software bug correction is considered successful. To evaluate the efficiency of the proposed model, it was compared with the baseline, and the proposed model showed better repair.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.580-583
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• 2018

본 논문에서는 SeqGAN 모델을 사용하여 한국어 시를 자동 생성해 보았다. SeqGAN 모델은 문장 생성을 위해 재귀 신경망과 강화 학습 알고리즘의 하나인 정책 그라디언트(Policy Gradient)와 몬테카를로 검색(Monte Carlo Search, MC) 기법을 생성기에 적용하였다. 시 문장을 자동 생성하기 위한 학습 데이터로는 사랑을 주제로 작성된 시를 사용하였다. SeqGAN 모델을 사용하여 자동 생성된 시는 동일한 구절이 여러번 반복되는 문제를 보였지만 한국어 텍스트 생성에 있어 SeqGAN 모델이 적용 가능함을 확인하였다.