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An Approach to Software Defect Prediction Combining Semantic Features and Code Changes

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2022年第9期32卷 1345-1368页

作者： Tao, Chuanqi Wang, Tao Guo, Hongjing Zhang, Jingxuan Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

Software defect prediction (SDP), which predicts defective code regions, can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts. Previous work on defect prediction has used machine learning and artificial software metrics. However, traditional defect prediction features extracted from artificial software metrics often fail to capture the syntactic and semantic information of defective modules. This work on defect prediction mostly focuses on abstract syntax tree (AST). Moreover, because current research on AST technology is relatively mature, it is difficult to further improve the accuracy of defect prediction when only using AST to characterize codes. In this paper, in order to capture more semantic features, we extract semantic information both from the sequences of AST tokens and code change tokens. In addition, to leverage the traditional features extracted from statistical metrics, we also combine the semantic features with traditional defect prediction features to perform SDP, and use the gated fusion mechanism to determine the combination ratio of the two kinds of features. In our empirical studies, 10 open-source Java projects from the PROMISE repository are chosen as our empirical subjects. Experimental results show that our proposed approach can perform better than several state-of-the-art baseline SDP methods.

关键词： Software defect prediction deep learning bi-directional long short-term memory network (BiLSTM) abstract syntax tree code change

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DFlow: A data flow analysis tool for C/C plus

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2021年第12期16卷 1635-1641页

作者： Yan, Qiaoqiao Li, Yongjun Wu, Yuanhao Zhou, Jialong Northwestern Polytech Univ Sch Comp Xian 710072 Shaanxi Peoples R China

.abstract syntax trees (ASTs), control flow graphs (CFGs), and data flow analysis (DFA) are prerequisites for static and dynamic analysis and vulnerability detection for programs;thus, obtaining them is significant. Recently, many tools related to generating ASTs, CFGs, and DFA have been proposed. However, most tools can only construct ASTs, very few can construct ASTs and CFGs, and almost none can construct all three. The vast majority of AST, CFG, and DFA tools are for other languages (e.g., Java and Python), and while a few are for C/C++, they are implemented in other languages, creating complex working environments, and overreliance on other language-related libraries. To address these shortcomings, we present a DFA tool, DFlow, for C/C++. First, a lexical/grammatical analyzer generated by Flex and Bison is used to analyze the program. Second, an AST is constructed from the results;then, a CFG is obtained from the analysis results and the information from the AST. Finally, based on the AST and CFG, DFA is performed, and the vulnerabilities of simple programs are determined. We test some common vulnerable code and common weakness enumeration slicing code, which show the effectiveness of DFlow in program data flow analysis and vulnerability checking. The results show that our tool can implement ASTs, CFGs, and DFA, and we add some rules to the tool for vulnerability detection. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： abstract syntax tree control flow graph data flow analysis

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Transforming MPI source code based on communication patterns

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2010年第1期26卷 147-154页

作者： Preissl, Robert Schulz, Martin Kranzlmueller, Dieter de Supinski, Bronis R. Quinlan, Daniel J. Johannes Kepler Univ Linz GUP Dept Graph & Parallel Proc Linz Austria Lawrence Livermore Natl Lab CASC Comp Sci Grp Livermore CA USA Lawrence Livermore Natl Lab ADEPT Adv Simulat & Comp Program Livermore CA USA Univ Munich Dept Comp Sci Munich Germany Bavarian Acad Sci Leibniz Supercomp Ctr LRZ Garching Germany

A high-level understanding of communication behavior can facilitate performance optimization of HPC applications. In particular, MPI communication patterns can serve as the basis of code transformations that substitute more efficient operations for those originally used by the application developer. We present a novel algorithm for identifying MPI communication patterns and a mechanism to perform source code transformations based on the observed patterns. Our transformation mechanism, implemented within the ROSE compiler framework, modifies the abstract syntax tree based on static analysis that confirms the generalizability of the patterns that we detect. Our results demonstrate the efficiency of our approach. Our pattern detection algorithm. which we evaluate with realistic applications using traces from up to 128 processors as well as with simple benchmarks, has low overhead as input or job size grows. Our transformation mechanism quickly optimizes MPI source code by replacing MPI code fragments for collective operations through highly optimized native MPI functions and by increasing the overlapping of communication and computation. (C) 2009 Elsevier B.V. All rights reserved.

关键词： MPI communication pattern ROSE compiler framework Source code transformation abstract syntax tree Suffix tree

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Generating Python Mutants From Bug Fixes Using Neural Machine Translation

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IEEE ACCESS 2023年 11卷 85678-85693页

作者： Asik, Sergen Yayan, Ugur Eskisehir Osmangazi Univ Dept Software Engn TR-26040 Eskisehir Turkiye Eskisehir Osmangazi Univ Autonomous Syst & Reliabil Lab TR-26040 Eskisehir Turkiye Eskisehir Osmangazi Univ Intelligent Factory & Robot Lab TR-26040 Eskisehir Turkiye

Due to the fast-paced development of technology, the software has become a crucial aspect of modern life, facilitating the operation and management of hardware devices. Nevertheless, using substandard software can result in severe complications for users, putting human lives at risk. This underscores the significance of error-free and premium-quality software. Verification and validation are essential in ensuring high-quality software development;software testing is integral to this process. Although code coverage is a prevalent method for assessing the efficacy of test suites, it has some limitations. Therefore, mutation testing is proposed as a remedy to tackle these limitations. Furthermore, mutation testing is recognized as a method for directing test case creation and evaluating the effectiveness of test suites. Our proposed method involves autonomously learning mutations from faults in real-world software applications. Firstly, our approach involves extracting bug fixes at the method-level, classifying them according to mutation types, and performing code abstraction. Subsequently, the approach utilizes a deep learning technique based on neural machine translation to develop mutation models. Our method has been trained and assessed using approximately similar to 588k bug fix commits extracted from GitHub. The results of our experimental assessment indicate that our models can forecast mutations resembling resolved bugs in 6% to 35% of instances. The models effectively revert fixed code to its original buggy version, reproducing the original bug and generating various other buggy codes with up to 94% accuracy. More than 96% of the generated mutants also demonstrate lexical and syntactic accuracy.

关键词： Software quality software verification and validation software testing mutation testing mining software repositories bug fixes abstract syntax tree deep learning neural machine translation evaluation of generated code quality

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Identifying typical approaches and errors in Prolog programming with argument-based machine learning

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 112卷 110-124页

作者： Mozina, Martin Lazar, Timotej Bratko, Ivan Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia

Students learn programming much faster when they receive feedback. However, in programming courses with high student-teacher ratios, it is practically impossible to provide feedback to all homeworks submitted by students. In this paper, we propose a data-driven tool for semi-automatic identification of typical approaches and errors in student solutions. Having a list of frequent errors, a teacher can prepare common feedback to all students that explains the difficult concepts. We present the problem as supervised rule learning, where each rule corresponds to a specific approach or error. We use correct and incorrect submitted programs as the learning examples, where patterns in abstract syntax trees are used as attributes. As the space of all possible patterns is immense, we needed the help of experts to select relevant patterns. To elicit knowledge from the experts, we used the argument-based machine learning (ABML) method, in which an expert and ABML interactively exchange arguments until the model is good enough. We provide a step-by-step demonstration of the ABML process, present examples of ABML questions and corresponding expert's answers, and interpret some of the induced rules. The evaluation on 42 Prolog exercises further shows the usefulness of the knowledge elicitation process, as the models constructed using ABML achieve significantly better accuracy than the models learned from human-defined patterns or from automatically extracted patterns. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Argument-based machine learning Rule learning Programming tutors abstract syntax tree Syntactic patterns

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AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models 22

AST-Probe: Recovering abstract syntax trees from hidden repr...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： José Antonio Hernández López Martin Weyssow Jesús Sánchez Cuadrado Houari Sahraoui Department of Computer Science and Systems University of Murcia Spain DIRO Université de Montréal Canada

ISBN: (纸本)9781450394758

The objective of pre-trained language models is to learn contextual representations of textual data. Pre-trained language models have become mainstream in natural language processing and code modeling. Using probes, a technique to study the linguistic properties of hidden vector spaces, previous works have shown that these pre-trained language models encode simple linguistic properties in their hidden representations. However, none of the previous work assessed whether these models encode the whole grammatical structure of a programming language. In this paper, we prove the existence of a syntactic subspace, lying in the hidden representations of pre-trained language models, which contain the syntactic information of the programming language. We show that this subspace can be extracted from the models’ representations and define a novel probing method, the AST-Probe, that enables recovering the whole abstract syntax tree (AST) of an input code snippet. In our experimentations, we show that this syntactic subspace exists in five state-of-the-art pre-trained language models. In addition, we highlight that the middle layers of the models are the ones that encode most of the AST information. Finally, we estimate the optimal size of this syntactic subspace and show that its dimension is substantially lower than those of the models’ representation spaces. This suggests that pre-trained language models use a small part of their representation spaces to encode syntactic information of the programming languages.

关键词： abstract syntax tree probing programming languages pre-trained language models

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A Cross-Project Defect Prediction Approach Based on Code Semantics and Cross-Version Structural Information

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2024年第7期34卷 1135-1171页

作者： Zou, Yifan Wang, Huiqiang Lv, Hongwu Zhao, Shuai Tian, Haoye Harbin Engn Univ Coll Comp Sci & Technol Harbin 150000 Peoples R China Univ Melbourne Grattan St Parkville Vic 3052 Australia

Context: Cross-project defect prediction (CPDP), due to the potential of adaption by industry in realistic scenarios, had gained significant attention from the research community. Currently, existing CPDP studies use static statistical features designed by experts, which might not capture the semantic and structural aspects of software, resulting in low accuracy in defect prediction. Meanwhile, they tend to overlook the valuable iterative information brought about by version updates in mature software ***: This paper introduces DETECTOR, a novel CPDP approach based on coDE semanTic and cross-vErsion struCTural infORmation to leverage cross-versions features of the software and improve the performance of ***: DETECTOR parses source code to exploit abstract syntax trees (ASTs) and cross-version software network (Cross-SN) that consists of internal class dependency network and cross-version class dependency edges. It utilizes Attention-based Bi-LSTM and simplified graph convolutional neural networks to automatically extract software features from ASTs and Cross-SN. The extracted features are fused using gate(& sdot;) to generate more effective cross-version features. Finally the source project is selected to carry out the data used to train the classifier to predict the ***: Empirical studies on seven open-source Java projects, the experiment results show that: (1) DETECTOR outperforms the state-of-the-art models in CPDP;(2) our proposed cross-version dependent edges positively contribute to DETECTOR performance;(3) gate(& sdot;) outperforms existing strategies in fusion features;(4) more multi-versions information enhance DETECTOR's ***: DETECTOR can predict more defects in CPDP and improve the accuracy and effectiveness of prediction.

关键词： Cross-project defect prediction deep learning abstract syntax tree class dependency network

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SEMI-STRUCTURED CURSOR MOVEMENTS IN MUPE-2

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SOFTWARE ENGINEERING JOURNAL 1989年第6期4卷 309-320页

作者： MADHAVJI, NH ZHANG, MJ BOULOS, S GU, YX MCGILL UNIV SCH COMP SCIMONTREAL H3A 2A7QUEBECCANADA

It has been recognised that cursor movements on a textual representation of a program present some problems when the movements are based on an underlying abstract syntax tree in a programming system. Approaches taken in many systems are to use structured or textual cursor movements, although both these approaches have some drawbacks. This paper presents a new approach, called semi-structured movements, based on the idea of cursor movement paths, or streams, that the cursor can follow in a program. The key benefits of this approach are that it eliminates the ‘treeness’ of structured movements and the flatness of textual movements, while providing an inherent support for program editing and browsing. Semi-structured cursor movements have been successfully implemented in the MUPE-2 programming environment, and this paper gives several scenarios in order to illustrate example cursor movements.

关键词： browsing software engineering trees (mathematics) streams Data handling techniques text editing Programming support textual representation MUPE-2 programming environment program editing Software engineering techniques programming environments abstract syntax tree semi-structured cursor movements paths

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Automated pattern-directed refactoring for complex conditional statements

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Journal of Central South University 2014年第5期21卷 1935-1945页

作者：刘伟胡志刚刘宏韬杨柳 School of Information Science and Engineering Central South University School of Software Central South University

Complex conditional statement is one of the bad code smells, which affects the quality of the code and design of software. In the proposed approach, two commonly-used design patterns for handling complex conditional statements are selected, and they are the factory method pattern and the strategy pattem. Two pattern-directed refactoring approaches based on the two design patterns are proposed. Each approach contains a refactoring opportunities identification algorithm and an automated refactoring algorithm. After parsing the abstract syntax tree generated from source code, the refactoring opportunities are identified effectively and automatically. Then, for candidate code, refactoring algorithms are executed automatically, which are used to simplify or remove complex conditional statements. By empirical analysis and quality assessment, the code after refactoring has better maintainability and extensibility, and the proposed approach for automated pattern-directed refactoring succeeds to reduce code size and complexity of classes.

关键词： refactoring abstract syntax tree complex conditional statements design patterns factory method pattem strategypattem

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Proximal Instance Aggregator networks for explainable security vulnerability detection

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 134卷 303-318页

作者： Hariharan, M. Kumar, Sathish C. Tanwar, Anshul Sundaresan, Krishna Ganesan, Prasanna Ravi, Sriram Karthik, R. Cisco Syst Pvt Ltd Bengaluru India Vellore Inst Technol Ctr Cyber Phys Syst Chennai India Vellore Inst Technol Sch Elect Engn Chennai India

Security vulnerabilities in software are the root cause of cyberattacks. Considering that these defects have huge associated costs, they should be proactively detected and resolved before shipping the software. Data-driven approaches like Artificial Intelligence (AI) are vastly explored for automatic vulnerability detection, given their potential to leverage large-scale vulnerability data feeds and learn from these scenarios. This work introduces a novel Proximal Instance Aggregator (PIA) neural network for accurately capturing insecure C code patterns from abstract syntax tree (AST). It is built upon the concept of Multiple Instance Learning (MIL), which treats the AST representation of the code as a 'bag' of tree path 'instances'. The security vulnerability can manifest in one or multiple such AST path instances. The PIA model dynamically learns a set of abstract concepts to describe the patterns associated with the AST paths. Specifically, the vulnerable nature of an AST path is characterized by its proximity to these concepts. The model also employs the attention mechanism to generate deep representations. By drawing cross-correlation of features between the path instances, the self-attention robustly weighs the relevance of each AST path towards vulnerability classification. The MIL utilizes these deep feature sets to construct the concept space. Thus, even without explicit supervision for localizing the line of defect, the AI automatically learns AST instance classification in a weakly supervised manner. Since AST-level prediction is formed as an aggregation of instance classifications, the AI is inherently explainable. The model outperforms state-of-the-art methods by a fair margin. It achieves 95.63% detection accuracy and 95.65% F1-score on the benchmarked NIST SARD, NVD datasets for a range of vulnerabilities. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Multiple-Instance learning Interpretability Deep learning Vulnerability detection abstract syntax tree Weakly supervised learning

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