2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)最新文献_第4页

Characterization and Prediction of Questions without Accepted Answers on Stack Overflow 基于堆栈溢出的未接受答案问题的表征与预测

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-21 DOI: 10.1109/ICPC52881.2021.00015

Mohamad Yazdaninia, David Lo, A. Sami

{"title":"Characterization and Prediction of Questions without Accepted Answers on Stack Overflow","authors":"Mohamad Yazdaninia, David Lo, A. Sami","doi":"10.1109/ICPC52881.2021.00015","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00015","url":null,"abstract":"A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable impact on the software development process. However, if developers do not get their expected answers, the websites will not be useful, and software development time will increase. Stack Overflow is the most popular CQA concerning programming problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is the user’s acceptance. In this paper, we investigate unresolved questions, without accepted answers, on Stack Overflow. The number of unresolved questions is increasing. As of August 2019, 47% of Stack Overflow questions were unresolved. In this study, we analyze the effectiveness of various features, including some novel features, to resolve a question. We do not use the features that contain information not present at the time of asking a question, such as answers. To evaluate our features, we deploy several predictive models trained on the features of 18 million questions to predict whether a question will get an accepted answer or not. The results of this study show a significant relationship between our proposed features and getting accepted answers. Finally, we introduce an online tool that predicts whether a question will get an accepted answer or not. Currently, Stack Overflow’s users do not receive any feedback on their questions before asking them, so they could carelessly ask unclear, unreadable, or inappropriately tagged questions. By using this tool, they can modify their questions and tags to check the different results of the tool and deliberately improve their questions to get accepted answers.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127650428","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: 10

Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach 利用方法名改进代码摘要:一种深思熟虑的多任务学习方法

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-21 DOI: 10.1109/ICPC52881.2021.00022

Rui Xie, Wei Ye, Jinan Sun, Shikun Zhang

{"title":"Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach","authors":"Rui Xie, Wei Ye, Jinan Sun, Shikun Zhang","doi":"10.1109/ICPC52881.2021.00022","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00022","url":null,"abstract":"Code summaries are brief natural language descriptions of source code pieces. The main purpose of code summarization is to assist developers in understanding code and to reduce documentation workload. In this paper, we design a novel multi-task learning (MTL) approach for code summarization through mining the relationship between method code summaries and method names. More specifically, since a method’s name can be considered as a shorter version of its code summary, we first introduce the tasks of generation and informativeness prediction of method names as two auxiliary training objectives for code summarization. A novel two-pass deliberation mechanism is then incorporated into our MTL architecture to generate more consistent intermediate states fed into a summary decoder, especially when informative method names do not exist. To evaluate our deliberation MTL approach, we carried out a large-scale experiment on two existing datasets for Java and Python. The experiment results show that our technique can be easily applied to many state-of-the-art neural models for code summarization and improve their performance. Meanwhile, our approach shows significant superiority when generating summaries for methods with non-informative names.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129939910","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: 19

Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community 通过代码审查理解代码气味检测:OpenStack社区的研究

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-21 DOI: 10.1109/ICPC52881.2021.00038

Xiaofeng Han, Amjed Tahir, Peng Liang, S. Counsell, Yajing Luo

{"title":"Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community","authors":"Xiaofeng Han, Amjed Tahir, Peng Liang, S. Counsell, Yajing Luo","doi":"10.1109/ICPC52881.2021.00038","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00038","url":null,"abstract":"Code review plays an important role in software quality control. A typical review process would involve a careful check of a piece of code in an attempt to find defects and other quality issues/violations. One type of issues that may impact the quality of the software is code smells - i.e., bad programming practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during code reviews. To investigate the concept behind code smells identified in code reviews and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews using the two most active OpenStack projects (Nova and Neutron). We manually checked 19,146 review comments obtained by keywords search and random selection, and got 1,190 smell-related reviews to study the causes of code smells and actions taken against the identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, and 4) developers generally followed those recommendations and actioned the changes. Our results suggest that 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, and 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project’s development team).","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133500374","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: 17

RAID: Tool Support for Refactoring-Aware Code Reviews RAID:支持重构意识代码审查的工具

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-21 DOI: 10.1109/ICPC52881.2021.00033

Rodrigo Brito, M. T. Valente

{"title":"RAID: Tool Support for Refactoring-Aware Code Reviews","authors":"Rodrigo Brito, M. T. Valente","doi":"10.1109/ICPC52881.2021.00033","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00033","url":null,"abstract":"Code review is a key development practice that contributes to improve software quality and to foster knowledge sharing among developers. However, code review usually takes time and demands detailed and time-consuming analysis of textual diffs. Particularly, detecting refactorings during code reviews is not a trivial task, since they are not explicitly represented in diffs. For example, a Move Function refactoring is represented by deleted (-) and added lines (+) of code which can be located in different and distant source code files. To tackle this problem, we introduce RAID, a refactoring-aware and intelligent diff tool. Besides proposing an architecture for RAID, we implemented a Chrome browser plug-in that supports our solution. Then, we conducted a field experiment with eight professional developers who used RAID for three months. We concluded that RAID can reduce the cognitive effort required for detecting and reviewing refactorings in textual diff. Besides documenting refactorings in diffs, RAID reduces the number of lines required for reviewing such operations. For example, the median number of lines to be reviewed decreases from 14.5 to 2 lines in the case of move refactorings and from 113 to 55 lines in the case of extractions.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126009026","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: 6

Understanding Architecture Erosion: The Practitioners’ Perceptive 理解建筑侵蚀:实践者的感知

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-21 DOI: 10.1109/ICPC52881.2021.00037

Ruiyin Li, Peng Liang, Mohamed Soliman, P. Avgeriou

{"title":"Understanding Architecture Erosion: The Practitioners’ Perceptive","authors":"Ruiyin Li, Peng Liang, Mohamed Soliman, P. Avgeriou","doi":"10.1109/ICPC52881.2021.00037","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00037","url":null,"abstract":"As software systems evolve, their architecture is meant to adapt accordingly by following the changes in requirements, the environment, and the implementation. However, in practice, the evolving system often deviates from the architecture, causing severe consequences to system maintenance and evolution. This phenomenon of architecture erosion has been studied extensively in research, but not yet been examined from the point of view of developers. In this exploratory study, we look into how developers perceive the notion of architecture erosion, its causes and consequences, as well as tools and practices to identify and control architecture erosion. To this end, we searched through several popular online developer communities for collecting data of discussions related to architecture erosion. Besides, we identified developers involved in these discussions and conducted a survey with 10 participants and held interviews with 4 participants. Our findings show that: (1) developers either focus on the structural manifestation of architecture erosion or on its effect on run-time qualities, maintenance and evolution; (2) alongside technical factors, architecture erosion is caused to a large extent by non-technical factors; (3) despite the lack of dedicated tools for detecting architecture erosion, developers usually identify erosion through a number of symptoms; and (4) there are effective measures that can help to alleviate the impact of architecture erosion.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114401488","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: 9

Let’s Ask Students About Their Programs, Automatically 让我们自动询问学生的课程

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-20 DOI: 10.1109/ICPC52881.2021.00054

T. Lehtinen, André L. M. Santos, Juha Sorva

引用次数: 14

Keywords Guided Method Name Generation 指导性方法名称生成

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-20 DOI: 10.1109/ICPC52881.2021.00027

Fan Ge, Li Kuang

{"title":"Keywords Guided Method Name Generation","authors":"Fan Ge, Li Kuang","doi":"10.1109/ICPC52881.2021.00027","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00027","url":null,"abstract":"High quality method names are descriptive and readable, which are helpful for code development and maintenance. The majority of recent research suggest method names based on the text summarization approach. They take the token sequence and abstract syntax tree of the source code as input, and generate method names through a powerful neural network based model. However, the tokens composing the method name are closely related to the entity name within its method implementation. Actually, high proportions of the tokens in method name can be found in its corresponding method implementation, which makes it possible for incorporating these common shared token information to improve the performance of method naming task. Inspired by this key observation, we propose a two-stage keywords guided method name generation approach to suggest method names. Specifically, we decompose the method naming task into two subtasks, including keywords extraction task and method name generation task. For the keywords extraction task, we apply a graph neural network based model to extract the keywords from source code. For the method name generation task, we utilize the extracted keywords to guide the method name generation model. We apply a dual selective gate in encoder to control the information flow, and a dual attention mechanism in decoder to combine the semantics of input code sequence and keywords. Experiment results on an open source dataset demonstrate that keywords guidance can facilitate method naming task, which enables our model to outperform the competitive state-of-the-art models by margins of 1.5%-3.5% in ROUGE metrics. Especially when programs share one common token with method names, our approach improves the absolute ROUGE-1 score by 7.8%.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116848547","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: 8

API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations 关于使用API文档改进自动生成代码注释

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-19 DOI: 10.1109/ICPC52881.2021.00049

Ramin Shahbazi, Rishab Sharma, F. H. Fard

{"title":"API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations","authors":"Ramin Shahbazi, Rishab Sharma, F. H. Fard","doi":"10.1109/ICPC52881.2021.00049","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00049","url":null,"abstract":"Code comments can help in program comprehension and are considered as important artifacts to help developers in software maintenance. However, the comments are mostly missing or are outdated, specially in complex software projects. As a result, several automatic comment generation models are developed as a solution. The recent models explore the integration of external knowledge resources such as Unified Modeling Language class diagrams to improve the generated comments. In this paper, we propose API2Com, a model that leverages the Application Programming Interface Documentations (API Docs) as a knowledge resource for comment generation. The API Docs include the description of the methods in more details and therefore, can provide better context in the generated comments. The API Docs are used along with the code snippets and Abstract Syntax Trees in our model.We apply the model on a large Java dataset of over 130,000 methods and evaluate it using both Transformer and RNN- base architectures. Interestingly, when API Docs are used, the performance increase is negligible. We therefore run different experiments to reason about the results. For methods that only contain one API, adding API Docs improves the results by 4% BLEU score on average (BLEU score is an automatic evaluation metric used in machine translation). However, as the number of APIs that are used in a method increases, the performance of the model in generating comments decreases due to long documentations used in the input. Our results confirm that the API Docs can be useful in generating better comments, but, new techniques are required to identify the most informative ones in a method rather than using all documentations simultaneously.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115367824","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: 16

Locating Faulty Methods with a Mixed RNN and Attention Model 基于混合RNN和注意力模型的故障定位方法

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-19 DOI: 10.1109/ICPC52881.2021.00028

Shouliang Yang, Junming Cao, Hushuang Zeng, Beijun Shen, Hao Zhong

{"title":"Locating Faulty Methods with a Mixed RNN and Attention Model","authors":"Shouliang Yang, Junming Cao, Hushuang Zeng, Beijun Shen, Hao Zhong","doi":"10.1109/ICPC52881.2021.00028","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00028","url":null,"abstract":"IR-based fault localization approaches achieves promising results when locating faulty files by comparing a bug report with source code. Unfortunately, they become less effective to locate faulty methods. We conduct a preliminary study to explore its challenges, and identify three problems: the semantic gap problem, the representation sparseness problem, and the single revision problem.To tackle these problems, we propose MRAM, a mixed RNN and attention model, which combines bug-fixing features and method structured features to explore both implicit and explicit relevance between methods and bug reports for method level fault localization task. The core ideas of our model are: (1) constructing code revision graphs from code, commits and past bug reports, which reveal the latent relations among methods to augment short methods and as well provide all revisions of code and past fixes to train more accurate models; (2) embedding three method structured features (token sequences, API invocation sequences, and comments) jointly with RNN and soft attention to represent source methods and obtain their implicit relevance with bug reports; and (3) integrating multi-revision bug-fixing features, which provide the explicit relevance between bug reports and methods, to improve the performance.We have implemented MRAM and conducted a controlled experiment on five open-source projects. Comparing with state-of-the-art approaches, our MRAM improves MRR values by 3.8-5.1% (3.7-5.4%) when the dataset contains (does not contain) localized bug reports. Our statistics test shows that our improvements are significant.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115112628","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: 7

Does Code Structure Affect Comprehension? On Using and Naming Intermediate Variables 代码结构影响理解吗?关于中间变量的使用和命名

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-03-19 DOI: 10.1109/ICPC52881.2021.00020

Roee Cates, Nadav Yunik, D. Feitelson

{"title":"Does Code Structure Affect Comprehension? On Using and Naming Intermediate Variables","authors":"Roee Cates, Nadav Yunik, D. Feitelson","doi":"10.1109/ICPC52881.2021.00020","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00020","url":null,"abstract":"Intermediate variables can be used to break complex expressions into more manageable smaller expressions, which may be easier to understand. But it is unclear when and whether this actually helps. We conducted an experiment in which subjects read 6 mathematical functions and were supposed to give them meaningful names. 113 subjects participated, of which 58% had 3 or more years of programming work experience. Each function had 3 versions: using a compound expression, using intermediate variables with meaningless names, or using intermediate variables with meaningful names. The results were that in only one case there was a significant difference between the two extreme versions, in favor of the one with intermediate variables with meaningful names. This case was the function that was the hardest to understand to begin with. In two additional cases using intermediate variables with meaningless names appears to have caused a slight decrease in understanding. In all other cases the code structure did not make much of a difference. As it is hard to anticipate what others will find difficult to understand, the conclusion is that using intermediate variables is generally desirable. However, this recommendation hinges on giving them good names.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144080","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: 5