SLDeep: Statement-level software defect prediction using deep-learning model on static code features

作者：

Highlights：

• We propose a suite of 32 statement-level metrics.

• We use long short-term memory (LSTM) as learning model.

• We have experimented on more than 100,000 C/C++ programs.

• We have achieved a recall of about 96% in the experiments.

摘要

•We propose a suite of 32 statement-level metrics.•We use long short-term memory (LSTM) as learning model.•We have experimented on more than 100,000 C/C++ programs.•We have achieved a recall of about 96% in the experiments.

论文关键词：Defect,Software fault proneness,Machine learning,Fault prediction model,Software metric

论文评审过程：Received 4 July 2019, Revised 23 November 2019, Accepted 20 December 2019, Available online 23 December 2019, Version of Record 8 January 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113156