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Refining software defect prediction through attentive neural models for code understanding

JOURNAL OF SYSTEMS AND SOFTWARE 2025年 220卷

作者： Nashaat, Mona Miller, James Port Said Univ Fac Engn Elect Engn Dept Port Said 42526 Egypt Univ Alberta Elect & Comp Engn Dept Edmonton AB T6G2R3 Canada

Identifying defects through manual software testing is a resource-intensive task in software development. To alleviate this, software defect prediction identifies code segments likely to contain faults using data-driven methods. Traditional techniques rely on static code metrics, which often fail to reflect the deeper syntactic and semantic features of the code. This paper introduces a novel framework that utilizes transformer-based networks with attention mechanisms to predict software defects. The framework encodes input vectors to develop meaningful representations of software modules. A bidirectional transformer encoder is employed to model programming languages, followed by fine-tuning with labeled data to detect defects. The performance of the framework is assessed through experiments across various software projects and compared against baseline techniques. Additionally, statistical hypothesis testing and an ablation study are performed to assess the impact of different parameter choices. The empirical findings indicate that the proposed approach can increase classification accuracy by an average of 15.93% and improve the F1 score by up to 44.26% compared to traditional methods.

关键词： Software Transformers code understanding Reliability Defect prediction

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Improving program comprehension by combining code understanding with comment understanding

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KNOWLEDGE-BASED SYSTEMS 2008年第8期21卷 813-825页

作者： Vinz, Bradley L. Etzkorn, Letha H. Univ Alabama Huntsville AL 35899 USA

Existing source code-based program comprehension approaches analyze either the code itself or the comments/identifiers but not both. In this research, we combine code understanding with comment and identifier understanding. This synergistic approach allows much deeper understanding of source code than is possible using either code or comments/identifiers alone. Our approach also allows comparing comments to their associated code to see whether they match or belong to the code. Our combined approach implements both Our heuristic code understanding and the comment/identifier understanding within the same knowledge base inferencing engine. This inferencing engine is the same used by an earlier well tested, mature comment/identifier-based program understanding approach. Published by Elsevier B.V.

关键词： Program comprehension code understanding Comment understanding Knowledge-based system Heuristic approaches

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An eye tracking study assessing the impact of background styling in code editors on novice programmers' code understanding 23

An eye tracking study assessing the impact of background sty...

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19th Annual ACM Conference on International Computing Education Research V.1 (ICER)

作者： Park, Kang-il Weill-Tessier, Pierre Brown, Neil C. C. Sharif, Bonita Jensen, Nikolaj Kolling, Michael Univ Nebraska Lincoln Lincoln NE 68583 USA Kings Coll London London England

ISBN: (纸本)9781450399760

Background and Context: The designers of programming editors aimed at learners have long experimented with different styles of code presentation. The idea of syntax highlighting - coloring specific words - is very old. More recently, some editors (including text-, frame- and block-based editors) have added forms of scope highlighting - colored rectangles to represent programming scope - but there have been few studies to investigate whether this is beneficial for novices when reading and understanding program code. Objectives: We investigated whether the use of scope highlighting during code comprehension tasks (a) has an impact on where users focus their gaze, (b) affects the accuracy of user's responses to tasks, and/or (c) affects the speed of user's correct responses to the tasks. Method: We conducted a controlled trial with a crossover design, where all participants completed the same twelve code comprehension tasks, each performed in one of three scope highlighting conditions. The conditions were (a) "plain" Java with no scope highlighting, (b) Java with BlueJ's scope highlighting, and (c) Stride, a frame-based language very similar to Java. We used a combination of eye-tracking hardware and video observation to record where users were looking in the code, the time it took them to answer the task, and the graded correctness of their answer. The data comes from students at two institutions in different countries. Findings: We found that there was no difference between plain Java, highlighted Java or Stride in terms of correctness, or speed to produce correct answers - and this was unaffected by whether users had seen the latter two interfaces before. There was a difference in eye gaze behavior, especially between Stride and the two Java interfaces. Participants' gaze moved around less, they had shorter saccade lengths, longer fixation durations, fewer regressions, and less line coverage in Stride, which could imply a higher cognitive load. Regardless of conditi

关键词： Java eye-tracking code understanding empirical study Stride

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Investigating the Impact of SOLID Design Principles on Machine Learning code understanding 24

Investigating the Impact of SOLID Design Principles on Machi...

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IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI (CAIN)

作者： Cabral, Raphael Kalinowski, Marcos Baldassarre, Maria Teresa Villamizar, Hugo Escovedo, Tatiana Lopes, Helio Pontificia Univ Catolica Rio de Janeiro Rio de Janeiro Brazil Univ Bari Bari Italy

ISBN: (纸本)9798400705915

[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists with diverse educational backgrounds, potentially resulting in code that doesn't adhere to software design best practices. [Goal] In order to better understand this phenomenon, we investigated the impact of the SOLID design principles on ML code understanding. [Method] We conducted a controlled experiment with three independent trials involving 100 data scientists. We restructured real industrial ML code that did not use SOLID principles. Within each trial, one group was presented with the original ML code, while the other was presented with ML code incorporating SOLID principles. Participants of both groups were asked to analyze the code and fill out a questionnaire that included both open-ended and closed-ended questions on their understanding. [Results] The study results provide statistically significant evidence that the adoption of the SOLID design principles can improve code understanding within the realm of ML projects. [Conclusion] We put forward that software engineering design principles should be spread within the data science community and considered for enhancing the maintainability of ML code.

关键词： SOLID Design Principles Machine Learning code understanding

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Investigating the Impact of SOLID Design Principles on Machine Learning code understanding 24

Investigating the Impact of SOLID Design Principles on Machi...

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23rd Brazilian Symposium on Software Quality

作者： Cabral, Raphael Kalinowski, Marcos Pontificia Univ Catolica Rio de Janeiro PUC Rio Dept Informat Rio De Janeiro RJ Brazil

ISBN: (纸本)9798400717772

[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists with diverse educational backgrounds, potentially resulting in code that doesn't adhere to software design best practices. [Goal] To better understand this phenomenon, we investigated the impact of the SOLID design principles on ML code understanding. [Method] We conducted a controlled experiment with three independent trials involving 100 data scientists. We restructured real industrial ML code that did not use SOLID principles. Within each trial, one group was presented with the original ML code, while the other was presented with ML code incorporating SOLID principles. Participants of both groups were asked to analyze the code and fill out a questionnaire that included both open-ended and closed-ended questions on their understanding. [Results] The dissertation results provide statistically significant evidence that adopting the SOLID design principles can improve code understanding within ML projects. [Conclusion] We put forward that software engineering design principles should be spread within the data science community and considered for enhancing the quality and maintainability of ML code.

关键词： SOLID Design Principles Machine Learning code understanding

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Test Script Intention Generation for Mobile Application via GUI Image and code understanding

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ACM Transactions on Software Engineering and Methodology 1000年

作者： Shengcheng Yu Chunrong Fang Jia Liu Zhenyu Chen State Key Laboratory for Novel Software Technology Nanjing University China

Testing is the most direct and effective technique to ensure software quality. Test scripts always play a more important role in mobile app testing than test cases for source code, due to the GUI-intensive and event-driven characteristics of mobile applications (app). Test scripts focus on user interactions and the corresponding response events, which is significant for testing the target app functionalities. Therefore, it is critical to understand the test scripts for better script maintenance and modification. There exist some mature code understanding (i.e., code comment generation, code summarization) technologies that can be directly applied to functionality source code with business logic. However, such technologies will have difficulties when being applied to test scripts, because test scripts are loosely linked to apps under test (AUT) by widget selectors, and do not contain business logic *** order to solve the test script understanding gap, this paper presents a novel approach, namely TestIntention, to infer the intention of GUI test scripts. Test intention refers to the user expectations of app behaviors for specific operations. TestIntention formalizes test scripts with an operation sequence model. For each operation within the sequence, TestIntention extracts the target widget selector and links the selector to the GUI layout information or the corresponding response events. For widgets identified by XPath, TestIntention utilizes the image understanding technologies to explore the detailed information of the widget images, the intention of which is understood with a deep learning model. For widgets identified by ID, TestIntention first maps the selectors to the response methods with business logic, and then adopts code understanding technologies to describe code in natural language form. Results of all operations are combined to generate test intention for test scripts. An empirical experiment including different metrics proves the outstanding

关键词： Mobile App Testing GUI understanding code understanding

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Specialized model initialization and architecture optimization for few-shot code search

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INFORMATION AND SOFTWARE TECHNOLOGY 2025年 177卷

作者： Zhang, Fan Wu, Qiang Peng, Manman Shen, Yuanyuan Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Hunan Prov Key Lab Blockchain Infrastructure & App Changsha 410082 Peoples R China North Univ China Sch Data Sci & Technol Taiyuan 030051 Peoples R China

Context: code search aims to find relevant code snippets from a codebase given a natural language query. It not only boosts developer efficiency but also improves the performance of tasks such as code generation and program repair, thus becoming one of the crucial tasks in software engineering. Objective: However, recent works are mainly designed for mainstream programming languages with abundant training data. We aim to address the challenges of code search for domain-specific programming languages with limited training data by proposing a novel two-stage, few-shot code search framework named SMIAO. Method: SMIAO includes a specialized model initialization and an architecture optimization stage. In the first stage, we first quantitatively identify a mainstream programming language's dataset that is semantically closest to a target few-shot programming language. Then, we enrich the dataset with hard samples and train an Adapter-GraphcodeBERT model to obtain well-initialized parameters. In the second stage, we first design a search space for the initialized Adapter-GraphcodeBERT model. Then, we employ neural architecture search to optimize the Adapter modules' positions and quantities in the GraphcodeBERT layers, tailoring for real-world few-shot code search tasks. Results: We conduct experiments on a publicly available dataset to demonstrate the effectiveness and rationality of SMIAO. The experimental results show that SMIAO outperforms other state-of-the-art baselines. Conclusion: Using mainstream languages' datasets to initialize Adapter-GraphcodeBERT models, followed by adjusting the quantities and positions of Adapter modules within the GraphcodeBERT layers by neural architecture search, can effectively improve the performance of few-shot code search tasks.

关键词： code search code understanding Information retrieval Neural architecture search AI for software engineering

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understanding the behaviour of hackers while performing attack tasks in a professional setting and in a public challenge

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EMPIRICAL SOFTWARE ENGINEERING 2019年第1期24卷 240-286页

作者： Ceccato, Mariano Tonella, Paolo Basile, Cataldo Falcarin, Paolo Torchiano, Marco Coppens, Bart De Sutter, Bjorn Fdn Bruno Kessler Trento Italy Fdn Bruno Kessler Software Engn Res Unit Trento Italy Politecn Torino Comp Engn Turin Italy Politecn Torino Control & Comp Engn Dept Turin Italy Univ East London Comp Sci London England Univ Ghent Ghent Belgium

When critical assets or functionalities are included in a piece of software accessible to the end users, code protections are used to hinder or delay the extraction or manipulation of such critical assets. The process and strategy followed by hackers to understand and tamper with protected software might differ from program understanding for benign purposes. Knowledge of the actual hacker behaviours while performing real attack tasks can inform better ways to protect the software and can provide more realistic assumptions to the developers, evaluators, and users of software protections. Within Aspire, a software protection research project funded by the EU under framework programme FP7, we have conducted three industrial case studies with the involvement of professional penetration testers and a public challenge consisting of eight attack tasks with open participation. We have applied a systematic qualitative analysis methodology to the hackers' reports relative to the industrial case studies and the public challenge. The qualitative analysis resulted in 459 and 265 annotations added respectively to the industrial and to the public challenge reports. Based on these annotations we built a taxonomy consisting of 169 concepts. They address the hacker activities related to (i) understanding code;(ii) defining the attack strategy;(iii) selecting and customizing the tools;and (iv) defeating the protections. While there are many commonalities between professional hackers and practitioners, we could spot many fundamental differences. For instance, while industrial professional hackers aim at elaborating automated and reproducible deterministic attacks, practitioners prefer to minimize the effort and try many different manual tasks. This analysis allowed us to distill a number of new research directions and potential improvements for protection techniques. In particular, considering the critical role of analysis tools, protection techniques should explicitly attack them, by

关键词： Software protection Software hacking Attack process Attack model Attack taxonomy Empirical study Hacker model Reverse engineering Analysis tools code understanding Defeat protections

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Student Program Classification Using Gated Graph Attention Neural Network

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IEEE ACCESS 2021年 9卷 87857-87868页

作者： Lu, M. Wang, Y. Tan, D. Zhao, L. Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Sch Geosci & Infophys Changsha 410083 Peoples R China

Source code mining has received increasing attention, among which code classification plays a significant role in code understanding and automatic coding. Most source code mining efforts aim at the source code of projects, which are usually large and standardized, but less for student programs. There are two differences between project codes and student programs. On the one hand, some work on project codes is based on relatively single information, which is far from enough for student programs. Because student programs are relatively small, which makes them contain less information. Consequently, it is necessary to mine as much information as possible in student programs. On the other hand, the variable or function naming and the structure of the student programs are usually irregular, as compared with the source codes of projects. To learn from student programs, we proposed a Graph Neural Network (GNN) based model, which integrates data flow and function call information to the Abstract Syntax Tree (AST), and applies an improved GNN model to the integrated graph to achieve the state-of-art student program classification accuracy. The experiment results have shown that the proposed work can classify student programs with accuracy over 97%.

关键词： Syntactics Logic gates Semantics Graph neural networks Task analysis Predictive models Natural language processing Abstract syntax tree attention mechanism code understanding function call graph data flow graph neural network student program classification

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On some end-user programming constructs and their understandability

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JOURNAL OF SYSTEMS AND SOFTWARE 2018年 142卷 206-222页

作者： Mackowiak, Michal Nawrocki, Jerzy Ochodek, Miroslaw Poznan Univ Tech Fac Comp Inst Comp Sci Ul Piotrowo 2 PL-60965 Poznan Poland

Context: End-user programming is becoming more and more important. However, existing programming paradigms and the languages based on them seem far-removed from what end-user programmers would need, especially in the area of Management Information. Objective: To evaluate the understandability of a set of programming constructs based on the spreadsheet metaphor from the point of view of end-user programmers in the context of Management Information. The examined set comprises single assignment with exemplary computations, data-driven iterations, selection by colours, and read-write heads (we refer to this as Board Programming). Method: A series of experiments was performed with students of Management Engineering, split into an experimental group and a control group. Each participant was given a piece of code expressed either with the proposed programming construct (experimental group) or its classical counterpart (control group). Their task was to predict the results. For the purpose of evaluation, the FACT indicators of understandability (First attempt failure rate, Attempt number, Cancellation ratio, prediction Time) were proposed and measured. Results: Three of the four examined features, i.e. single assignment with exemplary computations, data driven iterations, and read-write heads, proved to increase understandability of the chosen programs with regard to three out of the four FACT indicators, and these results were statistically significant. Selection by colours was not as effective as expected: the FACT indicator values were improved by that feature, but the difference was not statistically significant. Conclusions: The described programming constructs appear to be an interesting option when designing an end-user programming language for domain experts in the field of Management Information. (C) 2018 Elsevier Inc. All rights reserved.

关键词： End-user programming code understanding Spreadsheets

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