SLDeep: Statement-level software defect prediction using deep-learning model on static code features. (1st June 2020)

• Record Type: Journal Article
• Authors: Majd, Amirabbas; Vahidi-Asl, Mojtaba; Khalilian, Alireza; Poorsarvi-Tehrani, Pooria; Haghighi, Hassan
• Is Part Of: Expert systems with applications. Volume 147(2020)
• Publication Date: 2020-06-01
• Subjects: Defect -- Software fault proneness -- Machine learning -- Fault prediction model -- Software metric; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113156
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Abstract: Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose a new technique called as Statement-Level software defect prediction using Deep-learning model (SLDeep). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). We conducted experiments using 119, 989 C/C++ programs within Code4Bench. The programs comprise 2, 356, 458 lines of code of which 292, 064 lines are faulty. The benchmark comprises a diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data (that is, fault-proneness of new statements) with average performance measures 0.979, 0.570, and 0.702 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer's burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable.