Software development support using source code corpus
| Project/Area Number |
15K00108
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Software
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| Research Institution | Nihon University |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | コード補完 / コード推薦 / RNN / ソースコード推薦 / 機械学習 |
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| Outline of Final Research Achievements |
Developers reuse existing source code or use libraries to develop effectively. In this study, we focus on the order of method invocation statements in existing source code and propose to suggest method invocation statements. This study proposed an approach to suggest method invocation statements using recurrent neural network. I have implemented the approach and conducted experiments to measure an accuracy with 10 open source software projects. I have investigated various parameters of recurrent neural network. This evaluation has shown that our approach is 38% accuracy in API code suggestion, it can correctly suggest the API with top 1 candidate.
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Report
(4 results)
Research Products
(5 results)
