2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)最新文献

Still Confusing for Bug-Component Triaging? Deep Feature Learning and Ensemble Setting to Rescue 仍然对bug -组件分类感到困惑?深度特征学习和集成设置拯救

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00046

Yanqi Su, Zheming Han, Zhipeng Gao, Zhenchang Xing, Qinghua Lu, Xiwei Xu

{"title":"Still Confusing for Bug-Component Triaging? Deep Feature Learning and Ensemble Setting to Rescue","authors":"Yanqi Su, Zheming Han, Zhipeng Gao, Zhenchang Xing, Qinghua Lu, Xiwei Xu","doi":"10.1109/ICPC58990.2023.00046","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00046","url":null,"abstract":"To speed up the bug-fixing process, it is essential to triage bugs into the right components as soon as possible. Given the large number of bugs filed everyday, a reliable and effective bug-component triaging tool is needed to assist this task. LR-BKG is the state-of-the-art toolkit for doing this. However, the suboptimal performance for recommending the right component at the first position (low Top-1 accuracy) limits its usage in practice. We thoroughly investigate the limitations of LR-BKG and find out the gap between the manual feature design of LR-BKG and the characteristics of bug reports causes such suboptimal performance. Therefore, we propose an approach, DEEPTRIAG, which uses the large scale pre-trained models to extract deep features automatically from bug reports (including bug summary and description), to fill this gap. DEEPTRIAG transforms bug-component triaging into a multi-classification task (CodeBERT-Classifier) and a generation task (CodeT5-Generator). Then, we ensemble the prediction results from them to improve the performance of bug-component triaging further. Extensive experimental results demonstrate the superior performance of DEEPTRIAG on bug-component triaging over LR-BKG. In particular, the overall Top-1 accuracy is improved from 56.2% to 68.3% on Mozilla dataset and from 51.3% to 64.1% on Eclipse dataset, which verifies the effectiveness and generalization of our approach on improving the practical usage for bug-component triaging.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115496967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools 太简单了吗?在基于维护的编程工具研究中使用的任务复杂性概念

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00040

Patrick Rein, Tom Beckmann, Eva Krebs, Toni Mattis, R. Hirschfeld

{"title":"Too Simple? Notions of Task Complexity used in Maintenance-based Studies of Programming Tools","authors":"Patrick Rein, Tom Beckmann, Eva Krebs, Toni Mattis, R. Hirschfeld","doi":"10.1109/ICPC58990.2023.00040","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00040","url":null,"abstract":"Researchers conducting studies on programming tools often make use of maintenance tasks. The complexity of these tasks can significantly influence how participants behave. At the same time, the complexity of tasks is difficult to pinpoint due to the many sources of complexity for maintenance tasks. As a result, researchers may struggle to deliberately decide in which regard their tasks should be complex and in which regard they should be simple.To help researchers deliberately influence task complexity, we discuss different factors of task complexity. We draw these factors from 23 selected and 39 surveyed studies on programming tools. We arrange the factors according to a task complexity model from ergonomics research that we adapt for maintenance tasks. We illustrate the application of the factors through an example critique of a task design. In the end, task complexity might always be too complex to be fully controlled. Nevertheless, we hope that our discussion helps other researchers to decide in which dimensions their tasks are complex and in which dimensions they want to keep them simple.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127214364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Well Static Type Checkers Work with Gradual Typing? A Case Study on Python 静态类型检查器如何与渐进类型工作?Python的案例研究

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00039

Wenjie Xu, Lin Chen, Chenghao Su, Yimeng Guo, Yanhui Li, Yuming Zhou, Baowen Xu

{"title":"How Well Static Type Checkers Work with Gradual Typing? A Case Study on Python","authors":"Wenjie Xu, Lin Chen, Chenghao Su, Yimeng Guo, Yanhui Li, Yuming Zhou, Baowen Xu","doi":"10.1109/ICPC58990.2023.00039","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00039","url":null,"abstract":"Python has become increasingly popular and widely used in many fields. Dynamic features of Python provide much convenience for developers. However, they can also cause many type-related bugs undetected until runtime, which increases the cost of maintenance. Static type checking is essential to find bugs early, and the introduction of gradual typing and type annotations makes it easier to perform static type analysis. However, it remains to be investigated how well gradual typing improves real bug detection. Therefore, we conducted a comprehensive study on three widely used checkers: MyPy, PyRight, and PyType. We used a benchmark containing 10 popular Python projects with 40 real type-related bugs. First, we performed static type checking on the projects with and without type annotations to evaluate the effectiveness of finding real bugs. Second, we manually analyzed the missing bugs and investigated the reasons. The results show that the three tools can detect 29 of the 40 studied bugs after annotating, while only 14 bugs are detected before annotating. We also found that type annotations can substantially improve the ability of static type checkers to detect real bugs. A detailed analysis of bugs missed by the checkers shows that: (i) the accuracy of type analysis is challenged when it comes to programs with complicated dynamic features, such as dynamically changing object’s attributes, even with annotations; (ii) the inaccurate type annotations can undermine the ability of static type checkers to detect real bugs; (iii) static type checkers have different checking strategies in some cases, which has an impact on real bug detection. Our study can not only enable developers to better understand static type checking and make better use of them but also guide future research.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128917331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Path Complexity Correlates with Source Code Comprehension Effort Indicators 路径复杂性与源代码理解努力指标相关

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00041

Sofiane Dissem, Eli Pregerson, Adi Bhargava, Josh Cordova, Lucas Bang

{"title":"Path Complexity Correlates with Source Code Comprehension Effort Indicators","authors":"Sofiane Dissem, Eli Pregerson, Adi Bhargava, Josh Cordova, Lucas Bang","doi":"10.1109/ICPC58990.2023.00041","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00041","url":null,"abstract":"We describe our work on the relationship between the asymptotic path complexity of a program, time-to-completion, subjective complexity, correctness performance, and levels of brain (de)activation in select Brodmann areas of the brain, as measured by fMRI, while a human subject attempts to understand the source code of a program. Asymptotic path complexity gives an asymptotic upper bound on how quickly the number of paths through a program grows with increasing execution depth. (De)activation levels in the studied Brodmann areas of the brain are known to correlate with different specific types of cognitive effort. We add the asymptotic path complexity metric to an existing fMRI-code-comprehension data set that compares common code metrics to cognitive effort. Our results show that, according to Kendall rank correlation, asymptotic path complexity has (1) better correlation than all other metrics with code comprehension task completion time, (2) better correlation than all other metrics with subjective participant complexity, (3) better correlation than all other metrics for brain areas responsible for semantic processing, (4) correlations comparable to lines of code and Halstead complexity, and better correlation than (McCabe’s) cyclomatic complexity and dependency degree for participant response correctness and for (de)activation levels in brain areas responsible for rational thought and extracting signal from noise, and, finally, (5) worse correlation than all metrics (except McCabe’s cyclomatic complexity) for brain areas responsible for motion plandisse, language and audio processing, and additional forms of semantic processing. Overall, our results indicate that path complexity is a useful metric for measuring many aspects of code comprehension effort.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114081983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RCGraph - A Tool to Integrate Readme and Commits through Temporal Knowledge Graphs 一个通过时态知识图集成自述和提交的工具

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00014

Akhila Sri Manasa Venigalla, Mir Sameed Ali, Nikhil Manjunath, S. Chimalakonda

{"title":"RCGraph - A Tool to Integrate Readme and Commits through Temporal Knowledge Graphs","authors":"Akhila Sri Manasa Venigalla, Mir Sameed Ali, Nikhil Manjunath, S. Chimalakonda","doi":"10.1109/ICPC58990.2023.00014","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00014","url":null,"abstract":"Readme files and commit logs carry important and useful project information, corresponding to project dependencies, project functionalities, additions, deletions, and so on. These two artifacts have been analysed separately to obtain project specific information corresponding to contribution guidelines, bug prediction and localisation. Linking the readme files with associated commits and further querying the linked data could help in assessing time stamp specific changes made to the readme files. Utilizing knowledge graph representation of data is observed to largely support querying and integration and extraction of data from heterogeneous sources. To this end, we present a tool to generate readme specific temporal knowledge graph, as a first step towards integrating readme files and commit logs. As commits contain temporal information of the changes, we see that overlaying this information over the corresponding text in readme files could help in arriving at a temporal knowledge graph (TKG). We present a case study of querying the TKG for one repository and further evaluate the tool on 10 repositories spanning across 10 programming languages on GitHub. For demo video, visit - https://youtu.be/4YOCDngf4bY. For website of the tool, visit - https://akhilasrimanasa.github.io/rcgraph/","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129626616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Prediction From Source Code Is Task and Domain Specific 来自源代码的性能预测是特定于任务和领域的

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00015

Markus Böck, Sarra Habchi, Mathieu Nayrolles, Jürgen Cito

{"title":"Performance Prediction From Source Code Is Task and Domain Specific","authors":"Markus Böck, Sarra Habchi, Mathieu Nayrolles, Jürgen Cito","doi":"10.1109/ICPC58990.2023.00015","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00015","url":null,"abstract":"Performance is key to the success and adoption of software systems. In video games, performance is commonly highlighted as one of the top quality concerns raised by players. To check the performance of their systems, development teams tend to rely on profiling and monitoring tools, which observe program executions to identify regressions. The usage of static analysis tools for this purpose has been so far limited. Lately, the success of Large Language Models in many code analytics tools led to attempts to leverage them in static performance analysis. These studies showed promising results in predicting runtime and regressions on large public datasets. In this paper, we evaluate the usability of such models in practice, and particularly in the domain of video games. We train a state-of-the-art neural network on the Code4Bench dataset to predict runtime regressions for programming competition programs, then evaluate its ability to generalize to new domains. Our results show that these models achieve great results (e.g. 95.73% accuracy for performance comparison) on the original domain for programs solving in-sample programming tasks, yet fail to generalize to out-of-sample tasks. Furthermore, we show that transfer techniques such as domain adversarial adaptation and model fine-tuning are not sufficient to transfer these models to the target industrial domain of AAA games.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116428845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improving Code Search with Multi-Modal Momentum Contrastive Learning 用多模态动量对比学习改进代码搜索

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00043

Zejian Shi, Yun Xiong, Yao Zhang, Zhijie Jiang, Jinjing Zhao, Lei Wang, Shanshan Li

{"title":"Improving Code Search with Multi-Modal Momentum Contrastive Learning","authors":"Zejian Shi, Yun Xiong, Yao Zhang, Zhijie Jiang, Jinjing Zhao, Lei Wang, Shanshan Li","doi":"10.1109/ICPC58990.2023.00043","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00043","url":null,"abstract":"Contrastive learning has recently been applied to enhancing the BERT-based pre-trained models for code search. However, the existing end-to-end training mechanism cannot sufficiently utilize the pre-trained models due to the limitations on the number and variety of negative samples. In this paper, we propose MoCoCS, a multi-modal momentum contrastive learning method for code search, to improve the representations of query and code by constructing large-scale multi-modal negative samples. MoCoCS increases the number and the variety of negative samples through two optimizations: integrating multi-batch negative samples and constructing multi-modal negative samples. We first build momentum contrasts for query and code, which enables the construction of large-scale negative samples out of a mini-batch. Then, to incorporate multi-modal code information, we build multi-modal momentum contrasts by encoding the abstract syntax tree and the data flow graph with a momentum encoder. Experiments on CodeSearchNet with six programming languages demonstrate that our method can further improve the effectiveness of pre-trained models for code search.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124442151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

REMS: Recommending Extract Method Refactoring Opportunities via Multi-view Representation of Code Property Graph REMS:通过代码属性图的多视图表示推荐提取方法重构机会

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00034

Di Cui, Qiangqiang Wang, Siqi Wang, Jianlei Chi, Jianan Li, Lu Wang, Qingshan Li

{"title":"REMS: Recommending Extract Method Refactoring Opportunities via Multi-view Representation of Code Property Graph","authors":"Di Cui, Qiangqiang Wang, Siqi Wang, Jianlei Chi, Jianan Li, Lu Wang, Qingshan Li","doi":"10.1109/ICPC58990.2023.00034","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00034","url":null,"abstract":"Extract Method is one of the most frequently performed refactoring operations for the decomposition of large and complex methods, which can also be combined with other refactoring operations to remove a variety of design flaws. Several Extract Method refactoring tools have been proposed based on the quantification of extraction criteria. To the best of our knowledge, state-of-the-art related techniques can be broadly divided into two categories: the first line is non-machine-learning-based approaches built on heuristics, and the second line is machine learning-based approaches built on historical data. Most of these approaches characterize the extraction criteria by deriving software metrics from fine-grained code properties. However, in most cases, these metrics can be challenging to concretize, and their selections and thresholds also largely rely on expert knowledge. Thus, in this paper, we propose an approach to automatically recommend Extract Method refactoring opportunities named REMS via mining multi-view representations from code property graph. We fuse various representations together using compact bilinear pooling and further train machine learning classifiers to guide the extraction of suitable lines of code as new method. We evaluate our approach on two publicly available datasets. The results show that our approach outperforms five state-of-the-art refactoring tools including GEMS, JExtract, SEMI, JDeodorant, and Segmentation in effectiveness and usefulness. Our approach demonstrates an increase of 29% in precision, 15% in recall, and 23% in f1-measure. The results also unveil practical suggestions and provide new insights that benefit additional extract-related refactoring techniques.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127657799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QTC4SO: Automatic Question Title Completion for Stack Overflow QTC4SO:自动问题标题完成堆栈溢出

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00011

Yanlin Zhou, Shaoyu Yang, Xiang Chen, Zichen Zhang, Jiahua Pei

{"title":"QTC4SO: Automatic Question Title Completion for Stack Overflow","authors":"Yanlin Zhou, Shaoyu Yang, Xiang Chen, Zichen Zhang, Jiahua Pei","doi":"10.1109/ICPC58990.2023.00011","DOIUrl":"https://doi.org/10.1109/ICPC58990.2023.00011","url":null,"abstract":"Question posts with low-quality titles often discourage potential answerers in Stack Overflow. In previous studies, researchers mainly focused on directly generating question titles by analyzing the contents of the posts. However, the quality of the generated titles is still limited by the information available in the post contents. A more effective way is to provide accurate completion suggestions when developers compose titles. Inspired by this idea, we are the first to study the problem of automatic question title completion for Stack Overflow and then propose a novel approach QTC4SO. Specifically, we first preprocess the gathered post titles to form incomplete titles (i.e., tip information provided by developers) for simulating the scene of this task. Then we construct the multi-modal input by concatenating the incomplete title with the post’s contents (i.e., the problem description and the code snippet). Later, we adopt multi-task learning to the question title completion task for multiple programming languages. Finally, we adopt a pre-trained model T5 to learn the title completion patterns automatically. To evaluate the effectiveness of QTC4SO, we gathered 164,748 high-quality posts from Stack Overflow by covering eight popular programming languages. Our empirical results show that compared with the approaches of directly generating question titles, our proposed approach QTC4SO is more practical in automatic and human evaluation. Therefore, our study provides a new direction for automatic question title generation and we hope more researchers can pay attention to this problem in the future.","PeriodicalId":376593,"journal":{"name":"2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114706386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Slicito: Using Computational Notebooks for Program Comprehension 使用计算笔记本进行程序理解

2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) Pub Date : 2023-05-01 DOI: 10.1109/ICPC58990.2023.00019

Robert Husák, J. Kofroň, F. Zavoral

引用次数: 0