Software defect prediction, which predicts defective code regions, can assist developers in finding bugs and prioritizing their testing efforts. Traditional defect prediction features often fail to capture the semantic differences between different programs. This degrades the performance of the prediction models built on these traditional features. Thus, the capability to capture the semantics in programs is required to build accurate prediction models. To bridge the gap between semantics and defect prediction features, we propose leveraging a powerful representation-learning algorithm, deep learning, to learn the semantic representations of programs automatically from source code files and code changes. Specifically, we leverage a deep belief network (DBN) to automatically learn semantic features using token vectors extracted from the programs' abstract syntax trees (AST) (for file-level defect prediction models) and source code changes (for change-level defect prediction models). We examine the effectiveness of our approach on two file-level defect prediction tasks (i.e., file-level within-project defect prediction and file-level cross-project defect prediction) and two change-level defect prediction tasks (i.e., change-level within-project defect prediction and change-level cross-project defect prediction). Our experimental results indicate that the DBN-based semantic features can significantly improve the examined defect prediction tasks. Specifically, the improvements of semantic features against existing traditional features (in F1) range from 2.1 to 41.9 percentage points for file-level within-project defect prediction, from 1.5 to 13.4 percentage points for file-level cross-project defect prediction, from 1.0 to 8.6 percentage points for change-level within-project defect prediction, and from 0.6 to 9.9 percentage points for change-level cross-project defect prediction.

Software defect prediction, which predicts defective code regions, can help developers find bugs and prioritize their testing efforts. To build accurate prediction models, previous studies focus on manually designing features that encode the characteristics of programs and exploring different machine learning algorithms. Existing traditional features often fail to capture the semantic differences of programs, and such a capability is needed for building accurate prediction models.

We propose and implement a new approach, Document-Assisted Symbolic Execution (DASE), to improve automated test generation and bug detection. DASE leverages natural language processing techniques and heuristics to analyze program documentation to extract input constraints automatically. DASE then uses the input constraints to guide symbolic execution to focus on inputs that are semantically more important.We evaluated DASE on 88 programs from 5 mature real-world software suites: COREUTILS, FINDUTILS, GREP, BINUTILS, and ELFTOOLCHAIN. DASE detected 12 previously unknown bugs that symbolic execution without input constraints failed to detect, 6 of which have already been confirmed by the developers. In addition, DASE increases line coverage, branch coverage, and call coverage by 14.2 -- 120.3%, 2.3 -- 167.7%, and 16.9 -- 135.2% respectively, which are 6.0 -- 21.1 percentage points (pp), 1.6 -- 18.9 pp, and 2.8 -- 20.1 pp increases. The accuracies of input constraint extraction are 97.8 -- 100%.

Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences. In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.