During software development and maintenance, vulnerability detection is an essential part of software quality assurance. Even though many program-analysis-based and machine-learning-based approaches have been proposed to automatically detect vulnerabilities, they rely on explicit rules or patterns defined by security experts and suffer from either high false positives or high false negatives. Recently, an increasing number of studies leverage deep learning techniques, especially Graph Neural Network (GNN), to detect vulnerabilities. These approaches leverage program analysis to represent the program semantics as graphs and perform graph analysis to detect vulnerabilities. However, they suffer from two main problems: (i) Existing GNN-based techniques do not effectively learn the structural and semantic features from source code for vulnerability detection. (ii) These approaches tend to ignore fine-grained information in source code. To tackle these problems, in this paper, we propose a novel vulnerability detection approach, namedMGVD(Multiple-Graph-BasedVulnerabilityDetection), to detect vulnerable functions. To effectively learn the structural and semantic features from source code,MGVDuses three different ways to represent each function into multiple forms, i.e., two statement graphs and a sequence of tokens. Then we encode such representations to a three-channel feature matrix. The feature matrix contains the structural feature and the semantic feature of the function. And we add a weight allocation layer to distribute the weights between structural and semantic features. To overcome the second problem,MGVDconstructs each graph representation of the input function using multiple different graphs instead of a single graph. Each graph focuses on one statement in the function and its nodes denote the related statements and their fine-grained code elements. Finally,MGVDleverages CNN to identify whether this function is vulnerable based on such feature matrix. We conduct experiments on 3 vulnerability datasets with a total of 30,341 vulnerable functions and 127,931 non-vulnerable functions. The experimental results show that our method outperforms the state-of-the-art by 9.68% – 10.28% in terms of F1-score.

During software development and maintenance, defect localization is an essential part of software quality assurance. Even though different techniques have been proposed for defect localization, i.e., information retrieval (IR)-based techniques and spectrum-based techniques, they can only work after the defect has been exposed, which can be too late and costly to adapt to the newly introduced bugs in the daily development. To assist developers to detect bugs in time and avoid introducing them, just-in-time (JIT) bug localization techniques have been proposed, which is targeting to locate suspicious buggy code after a change commit has been submitted. In this paper, we propose a novel JIT defect localization approach, named DeepDL (Deep Learning-based defect localization), to locate defect code lines within a defect introducing change. DeepDL employs a neural language model to capture the semantics of the code lines, in this way, the naturalness of each code line can be learned and converted to a suspiciousness score. The core of our DeepDL is a deep learning-based neural language model. We train the neural language model with previous snapshots (history versions) of a project so that it can calculate the naturalness of a piece of code. In its application, for a given new code change, DeepDL automatically assigns a suspiciousness score to each code line and sorts these code lines in descending order of this score. The code lines at the top of the list are considered as potential defect locations. Our tool can assist developers efciently check buggy lines at an early stage, which is able to reduce the risk of introducing bugs in time and improve the developers condence in the reliability of their software. We conducted an extensive experiment on 14 open source Java projects with a total of 11,615 buggy changes. We evaluate the experimental results considering four evaluation metrics. The experimental results show that our method outperforms the state-of-the-art by a substantial margin.

In software development and maintenance, defect localization is necessary for software quality assurance. Current defect localization techniques mainly rely on defect symptoms (e.g., bug reports or program spectrum) when the defect has been exposed. One challenge task is: can we locate buggy program prior to the appearance of the defect symptom. Such kind of localization is conducted at an early stage (e.g., when buggy program elements are being checked-in) which can be an early step of continuous quality control.