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Studying logging practice in test code

EMPIRICAL SOFTWARE ENGINEERING 2022年第4期27卷 1-45页

作者： Zhang, Haonan Tang, Yiming Lamothe, Maxime Li, Heng Shang, Weiyi Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada Polytech Montreal Dept Comp Engn & Software Engn Montreal PQ Canada

Logging is widely used in modern software development to record run-time information for software systems and plays a significant role in software testing. Although the research area of logging has attracted much attention, little attention is paid to the practice of test logging (i.e., the logging involved in test files). To fill this knowledge gap, we conduct this empirical study to explore and disclose the practice of test logging. This study examines 21 open-source subjects with similar to 70K logging statements, of which similar to 48K are production logging statements and similar to 22K are test logging statements. We organize our study by answering four research questions, and as a result, (1) we have yielded five findings to reveal the differences between test and production logging statements, (2) we have disclosed four findings regarding the differences between the maintenance efforts of test and production logging statements, (3) we have identified four reasons why developers use test log, and (4) we have uncovered the relationship between test logging and production logging. To the best of our knowledge, this is the first study that quantitatively and qualitatively analyzes the logging practices in test and production code, providing developers and researchers with insight into this topic.

关键词： Software logs Logging practices Software testing Mining software repository source code analysis

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DAPP: automatic detection and analysis of prototype pollution vulnerability in *** modules

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2022年第1期21卷 1-23页

作者： Kim, Hee Yeon Kim, Ji Hoon Oh, Ho Kyun Lee, Beom Jin Mun, Si Woo Shin, Jeong Hoon Kim, Kyounggon Korea Univ Dept Cyber Def Seoul South Korea Hayyim Secur Seoul South Korea Alice & Mallory Seoul South Korea THEORI Seoul South Korea Naif Arab Univ Secur Sci Dept Forens Sci Riyadh Saudi Arabia

The safe maintenance of *** modules is critical in the software security industry. Most server-side web applications are built on ***, an environment that is highly dependent on modules. However, there is clear lack of research on *** module security. This study focuses particularly on prototype pollution vulnerability, which is an emerging security vulnerability type that has also not been studied widely. To this point, the main goal of this paper is to propose patterns that can identify prototype pollution vulnerabilities. We developed an automatic static analysis tool called DAPP, which targets all the real-world modules registered in the Node Package Manager. DAPP can discover the proposed patterns in each *** module in a matter of a few seconds, and it mainly performs and integrates a static analysis based on abstract syntax tree and control flow graph. This study suggests an improved and efficient analysis methodology. We conducted multiple empirical tests to evaluate and compare our state-of-the-art methodology with previous analysis tools, and we found that our tool is exhaustive and works well with modern JavaScript syntax. To this end, our research demonstrates how DAPP found over 37 previously undiscovered prototype pollution vulnerabilities among 30,000 of the most downloaded *** modules. To evaluate DAPP, we expanded the experiment and ran our tool on 100,000 *** modules. The evaluation results show a high level of performance for DAPP along with the root causes for false positives and false negatives. Finally, we reported the 37 vulnerabilities, respectively, and obtained 24 CVE IDs mostly with 9.8 CVSS scores.

关键词： Node js security Automatic vulnerability extrapolation source code analysis Prototype pollution

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MIXcode: Enhancing code Classification by Mixup-Based Data Augmentation 30

MIXCODE: Enhancing Code Classification by Mixup-Based Data A...

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30th IEEE International Conference on Software analysis, Evolution and Reengineering (SANER)

作者： Dong, Zeming Hu, Qiang Guo, Yuejun Cordy, Maxime Papadakis, Mike Zhang, Zhenya Le Traon, Yves Zhao, Jianjun Kyushu Univ Fukuoka Japan Univ Luxembourg Luxembourg Luxembourg Luxembourg Inst Sci & Technol Luxembourg Luxembourg

ISBN: (纸本)9781665452786

Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software engineering community. Due to its data-driven nature, a DNN model requires massive and high-quality labeled training data to achieve expert-level performance. Collecting such data is often not hard, but the labeling process is notoriously laborious. The task of DNN-based code analysis even worsens the situation because source code labeling also demands sophisticated expertise. Data augmentation has been a popular approach to supplement training data in domains such as computer vision and NLP. However, existing data augmentation approaches in code analysis adopt simple methods, such as data transformation and adversarial example generation, thus bringing limited performance superiority. In this paper, we propose a data augmentation approach MIXcode that aims to effectively supplement valid training data, inspired by the recent advance named Mixup in computer vision. Specifically, we first utilize multiple code refactoring methods to generate transformed code that holds consistent labels with the original data. Then, we adapt the Mixup technique to mix the original code with the transformed code to augment the training data. We evaluate MIXcode on two programming languages (Java and Python), two code tasks (problem classification and bug detection), four benchmark datasets (JAVA250, Python800, CodRepl, and Refactory), and seven model architectures (including two pre-trained models codeBERT and GraphcodeBERT). Experimental results demonstrate that MIXcode outperforms the baseline data augmentation approach by up to 6.24% in accuracy and 26.06% in robustness.

关键词： Data augmentation Mixup source code analysis

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Chaos to Clarity with Semantic Inferencing for Python source code Snippets 17

Chaos to Clarity with Semantic Inferencing for Python Source...

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17th IEEE International Conference on Semantic Computing (ICSC)

作者： Stein, Aviel Mancoridis, Spiros Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA

ISBN: (纸本)9781665482639

Our work investigates using semantic indicators in source code to generate descriptions of its functionality. We applied natural language processing (NLP) techniques to improve the performance of a neural machine translation (NMT) model on source code. We trained the model on 20,000 code-comment pairs from the source code analysis Dataset (SCAD) using a custom Python source code tokenizer and a learning model with attention, RNNs, LSTMs, and auto-encoders. We evaluated the model's performance on a hold-out dataset using several established translation metrics, and found that our methods outperform prior work with a mean Bilingual Evaluation Understudy (BLEU) score of 0.47.

关键词： natural language structured data deep learning semantic inference source code analysis

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Towards the Assessment of Basic Computational Thinking Skills Using Syntactic analysis Techniques

Towards the Assessment of Basic Computational Thinking Skill...

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2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Gonzalez-Torres, Antonio Ramirez-Trejos, Elliot Sancho-Chavarria, Lilliana Navas-Su, Jose Garita, Cesar Monge-Fallas, Jorge Costa Rica Institute of Technology Computer Engineering Cartago Costa Rica School of Mathematics Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (纸本)9798350327595

This article introduces an exploratory method for automatically grading programming exam questions using syntactic analysis. The target problem is the lack of a robust, scalable, and automated method to analyze computational thinking skills from source code written by elementary school students. The proposed method uses a variety of techniques to assess student responses, including analyzing the programming structure, programming correctness, and code execution based on certain parameters defined during the exercise specification. analysis of the source code and evaluation of the answers to the exercises are carried out using high performance computing to improve the response time of the system. This preliminary work will contribute to a robust method for automated exam scoring, which is expected to assess and support the development of computational thinking among students. © 2023 IEEE.

关键词： abstract syntax trees Automatic evaluation computational thinking source code analysis

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Studying logging practice in machine learning-based applications

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INFORMATION AND SOFTWARE TECHNOLOGY 2024年 170卷

作者： Foalem, Patrick Loic Khomh, Foutse Li, Heng Polytech Montreal Dept Comp & Software Engn Montreal PQ H3T 1J4 Canada

Context: Logging is a common practice in traditional software development. There have been multiple studies on the characteristics of logging in traditional software systems such as C/C++, Java, and Android applications. However, logging practices in Machine Learning -based (ML -based) applications are still not well understood. The size and complexity of data and models used in ML -based applications present unique challenges for logging. Objective: In this paper, we aim to bridge this knowledge gap and provide insight into the logging practices in ML -based applications, making the first attempt to characterize current logging practices within a large number of open -source ML -based applications. Method: We conducted an empirical study on 502 open -source ML applications to understand their logging practices, combining quantitative and qualitative analyses and a survey involving 31 practitioners. Results: Our quantitative analysis reveals that logging in ML applications is less common than in traditional software, with info and warn log levels being popular. Top ML -specific logging libraries include MLflow, Tensorboard, Neptune, and W&B. Qualitatively, logging is used for data and model management, especially in model training. Our survey reinforces the importance of logging in experiment tracking, complementing our qualitative findings. Conclusion: Our research carries significant implications. It reveals distinctive ML logging practices compared to traditional software. We have highlighted the prevalence of general-purpose logging libraries in ML code, indicating a potential gap in awareness regarding ML -specific logging tools. This insight benefits researchers and developers aiming to enhance ML project reproducibility and sets the stage for exploring ML -specific logging tools' impact on machine learning system quality and trustworthiness.

关键词： Logging practices ML -based applications Mining software repositories source code analysis

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Enhancing HPC System Log analysis by Identifying Message Origin in source code 29

Enhancing HPC System Log Analysis by Identifying Message Ori...

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29th IEEE International Symposium on Software Reliability Engineering (ISSRE)

作者： Hickman, Megan Fulp, Dakota Baseman, Elisabeth Blanchard, Sean Greenberg, Hugh Jones, William DeBardeleben, Nathan Los Alamos Natl Lab Ultrascale Syst Res Ctr High Performance Comp Los Alamos NM 87545 USA Coastal Carolina Univ Dept Comp Sci Conway SC 29528 USA

ISBN: (纸本)9781538694435

Supercomputers, high performance computers, and clusters are composed of very large numbers of independent operating systems that are generating their own system logs. Messages are generated locally on each host and usually are transferred to a central logging infrastructure which keeps a master record of the system as a whole. At Los Alamos National Laboratory (LANL) a collection of open source cloud tools are used which log over a hundred million system log messages per day from over a dozen such systems. Understanding what source code created those messages can be extremely useful to system administrators when they are troubleshooting these complex systems as it can give insight into a subsystem (disk, network, etc.) or even line numbers of source code. Oftentimes, debugging supercomputers is done in environments where open access cannot be provided to all individuals due to security concerns. As such, providing a means for conveying information between system log messages and source code lines allows for communication between system administrators and source developers or supercomputer vendors. In this work, we demonstrate a prototype tool which aims to provide such an expert system. We leverage capabilities from ElasticSearch, one of the open source cloud tools deployed at LANL, and with our own metrics develop a means for correctly matching source code lines as well as files with high confidence. We discuss confidence metrics and show that in our experiments 92% of syslog lines were correctly matched. For any future samples, we predict with 95% confidence that the correct file will be detected between 88.2% and 95.8% of the time. Finally, we discuss enhancements that are underway to improve the tool and study it on a larger dataset.

关键词： system logs source code analysis ElasticSearch Logan expert system

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A survey on machine learning techniques applied to source code

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 209卷

作者： Sharma, Tushar Kechagia, Maria Georgiou, Stefanos Tiwari, Rohit Vats, Indira Moazen, Hadi Sarro, Federica Dalhousie Univ Halifax NS Canada UCL London England Queens Univ Kingston ON Canada DevOn Bangalore India JSS Acad Tech Educ Bengaluru India Sharif Univ Technol Tehran Iran

The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number of studies hinders the community from understanding the current research landscape. This paper aims to summarize the current knowledge in applied machine learning for source code analysis. We review studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we conducted an extensive literature search and identified 494 studies. We summarize our observations and findings with the help of the identified studies. Our findings suggest that the use of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task and summarize machine learning techniques employed. We identify a comprehensive list of available datasets and tools useable in this context. Finally, the paper discusses perceived challenges in this area, including the availability of standard datasets, reproducibility and replicability, and hardware resources. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

关键词： Machine learning for software engineering source code analysis Deep learning Datasets Tools

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Semantic code Clone Detection Method for Distributed Enterprise Systems 12

Semantic Code Clone Detection Method for Distributed Enterpr...

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12th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Svacina, Jan Bushong, Vincent Das, Dipta Cerny, Tomas Baylor Univ Comp Sci One Bear Pl Waco TX 76798 USA

ISBN: (纸本)9789897585708

Conventional approaches to code clone detection consider systems from elementary construct perspectives, making it difficult to detect semantic clones. This paper argues that semantic clone detection could be improved for enterprise systems since they typically use well-established architectures and standards. Semantic clone detection is crucial for enterprises where software's codebase grows and evolves and maintenance costs rise significantly. While researchers have proposed many code clone detection techniques, there is a lack of solutions targeted explicitly toward enterprise systems and even fewer solutions dedicated to semantic clones. Semantic clones exhibit the same behavior between clone pairs but differ in the syntactic structure. This paper proposes a novel approach to detect semantic clones for enterprise frameworks. The driving idea is to transform a particular enterprise application into a control-flow graph representation. Next, various proprietary similarity functions are applied to compare targeted enterprise metadata for each pair of the control-flow graph fragment. As a result, we achieve to detect semantic clones with high accuracy and reasonable time complexity.

关键词： source code analysis code Clone Detection Semantic Clone Enterprise Applications code Smells

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codeProcess Charts: Visualizing the Process of Writing code 22

CodeProcess Charts: Visualizing the Process of Writing Code

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24th Australasian Computing Education Conference (ACE)

作者： Shrestha, Raj Leinonen, Juho Hellas, Arto Ihantola, Petri Edwards, John Utah State Univ Logan UT 84322 USA Univ Helsinki Helsinki Finland Aalto Univ Espoo Finland

ISBN: (纸本)9781450396431

Instructors of computer programming courses evaluate student progress on code submissions, exams, and other activities. The evaluation of code submissions is typically a summative assessment that gives very little insight into the process the student used when designing and writing the code. Thus, a tool that offers instructors a view into how students actually write their code could have broad impacts on assessment, intervention, instructional design, and plagiarism detection. In this article we propose an interactive software tool with a novel visualization that includes both static and dynamic views of the process that students take to complete computer programming assignments. We report results of an exploratory think-aloud study in which instructors offer thoughts as to the utility and potential of the tool. In the think-aloud study, we observed that the instructors easily identified multiple coding strategies (or the lack of thereof), were able to recognize plagiarism, and noticed a clear need for wider dissemination of tools for visualizing the programming process.

关键词： process data visualization software visualization tool visualization tool source code analysis source code snapshots learning analytics educational data mining

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