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

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective 在哪里处理异常?从全局角度推荐异常处理位置

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00042

Xiangyang Jia, Songqiang Chen, Xingqi Zhou, Xintong Li, Runze Yu, Xu Chen, J. Xuan

{"title":"Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective","authors":"Xiangyang Jia, Songqiang Chen, Xingqi Zhou, Xintong Li, Runze Yu, Xu Chen, J. Xuan","doi":"10.1109/ICPC52881.2021.00042","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00042","url":null,"abstract":"Exception handling is an effective mechanism to guarantee software reliability in modern programming languages. An exception interrupts the program execution and propagates backwards along the call chain until the exception is caught by an exception handler. In software development practices, developers may be confused in determining where to place the exception handler in the call chain. The reason is that exception handling requires a developer to take a comprehensive consideration from a global perspective of the software project. In this paper, we propose an automatic approach EHAdvisor, which recommends exception handling locations from the global perspective of the project. EHAdvisor first trains a binary classification model based on four types of features, including architectural features, project features, functional features, and exception features. Then, for a new code snippet with exceptions, EHAdvisor predicts the exception catching probability for each method in the call chain based on the classification model and recommends Top-K exception handling locations based on the probability ranking. We conducted experiments on a dataset from 29 high-quality open source projects. Experimental results show that EHAdvisor achieves an average Top-1 recommendation success rate of 70.83% for across-project location recommendation and an average Top-1 accuracy of 86.21% for intra-project recommendation. Experiments on the importance scores show that global features, such as project features and architectural features, are evidently important to the recommendation of exception handling locations.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133056870","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: 3

Is Algorithm Comprehension Different from Program Comprehension? 算法理解不同于程序理解?

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00053

Philipp Kather, J. Vahrenhold

{"title":"Is Algorithm Comprehension Different from Program Comprehension?","authors":"Philipp Kather, J. Vahrenhold","doi":"10.1109/ICPC52881.2021.00053","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00053","url":null,"abstract":"At the beginning of their undergraduate studies, computer science students are exposed to introductory programming, discrete mathematics, and algorithms. A large body of literature has studied how such students comprehend programs. Much less attention has been paid to understanding how students comprehend algorithms, i.e., representations of problem-solving processes accompanied by proofs of their properties. To investigate whether algorithm comprehension is under-researched or whether we can simply transfer results from program comprehension (and, possibly, proof comprehension) to the domain of algorithms, we conducted a larger study following a Grounded-Theory approach. For this study, we worked with twelve participants with varying degrees of prior knowledge regarding algorithms, from technically no prior knowledge to advanced graduate students pursuing a doctoral degree in algorithms-related fields. In this paper, we report on the analysis and interpretation of interview data focused on exploring potential differences between program comprehension and algorithm comprehension. Our analyses and interpretations revealed both similarities and differences between program comprehension and algorithm comprehension: Unsurprisingly, we found that aspects known from program comprehension, e.g., using top-down approaches or utilizing prior knowledge of programming plans, can be found in algorithm comprehension as well. However, some of these aspects manifest themselves in notably different details or are influenced by, e.g., the wording of a proof. Most interesting, we found that novice and advanced students alike benefit from switching between reading the representation of the program-solving process and the accompanying proofs. We present qualitative results illustrating both similarities and differences between program comprehension and algorithm comprehensions and offer first explanations for these.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133631920","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: 4

The Effect Of Crowding On The Reading Of Program Code For Programmers With Dyslexia 拥挤对读写困难程序员阅读程序代码的影响

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00036

I. McChesney, R. Bond

{"title":"The Effect Of Crowding On The Reading Of Program Code For Programmers With Dyslexia","authors":"I. McChesney, R. Bond","doi":"10.1109/ICPC52881.2021.00036","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00036","url":null,"abstract":"Good program layout and consistent application of style facilitates code readability and comprehension. Appropriate use of white space, in particular vertical space, is useful for organising code into logical groupings of text. Where this style is not followed then the code manifests crowding and can inhibit comprehension. When reading natural text, crowding has been recognised as disproportionately affecting the reading efficiency of dyslexic readers. We present an independent two-factorial study which examines the extent to which crowding in program code affects programmers with dyslexia. The study involved 30 participants (14 dyslexia, 16 control) reading and describing crowded and spaced versions of three Java programs. Comprehension time and accuracy were measured. An eye tracker was used to collect gaze metrics. Results are presented relating to the interaction between dyslexia and crowding. Noting the small sample size, the results show that, while there is an interaction effect on gaze metrics for some program features, the results do not suggest any significant effect whereby programmers with dyslexia are disproportionately affected by crowding in computer programs.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127877064","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: 3

Interactive Static Software Performance Analysis in the IDE IDE中的交互式静态软件性能分析

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00057

Aaron Beigelbeck, M. Aniche, Jürgen Cito

{"title":"Interactive Static Software Performance Analysis in the IDE","authors":"Aaron Beigelbeck, M. Aniche, Jürgen Cito","doi":"10.1109/ICPC52881.2021.00057","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00057","url":null,"abstract":"Detecting performance issues due to suboptimal code during the development process can be a daunting task, especially when it comes to localizing them after noticing performance degradation after deployment. Static analysis has the potential to provide early feedback on performance problems to developers without having to run profilers with expensive (and often unavailable) performance tests. We develop a VSCode tool that integrates the static performance analysis results from Infer via code annotations and decorations (surfacing complexity analysis results in context) and side panel views showing details and overviews (enabling explainability of the results). Additionally, we design our system for interactivity to allow for more responsiveness to code changes as they happen. We evaluate the efficacy of our tool by measuring the overhead that the static performance analysis integration introduces in the development workflow. Further, we report on a case study that illustrates how our system can be used to reason about software performance in the context of a real performance bug in the ElasticSearch open-source project.Demo video: https://www.youtube.com/watch?v=-GqPb_YZMOs Repository: https://github.com/ipa-lab/vscode-infer-performance","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114297788","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

Warning-Introducing Commits vs Bug-Introducing Commits: A tool, statistical models, and a preliminary user study 引入警告的提交vs引入错误的提交:一个工具、统计模型和初步的用户研究

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00051

Louis-Philippe Querel, Peter C. Rigby

{"title":"Warning-Introducing Commits vs Bug-Introducing Commits: A tool, statistical models, and a preliminary user study","authors":"Louis-Philippe Querel, Peter C. Rigby","doi":"10.1109/ICPC52881.2021.00051","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00051","url":null,"abstract":"This paper partially replicates prior works on building historical commits [1], commit risk modeling [2], and a comparison of statistical bug models and static bug finders [3].We examine 8 Maven-based projects with an average lifespan of 5.8 years. To historically build these projects across a total of 45k commits, we develop a series of techniques, such as flexibly selecting the version of a library that is closest to the commit date. We are able to build a per project average of 78.4% of all commits, a doubling in buildability compared to prior work. We also develop a git blame strategy to assign warnings even when a commit does not build.We run JLint and FindBugs and create a logistic regression model to predict if a commit that introduces a warning has higher odds of introducing a bug. The static bug finders model accounted for only 13% of the deviance, while the statistical bug model accounted for 19.5%. We had expected static bug finder warnings to improve the predictive power of models of bug introducing changes, but we clearly attained a negative result.To understand this negative result, we perform a preliminary user study of developers who introduced new warnings in 37 projects. We found that while warnings might not predict bugs, 53% and 21% of warnings in Findbugs and Jlint respectively are useful. We also study whether just-in-time warnings presentation on each commit impacted usefulness. We find that the later a warning is shown to a developer, the less useful it is perceived to be (a median of 11.5 days versus 23 days for non useful warnings).Based on our findings, we modify the existing COMMITGURU interface to add new warnings to the specific line in a changed file. The empirical study data [4] and WARNINGSGURU tool [5] are publicly available.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133077937","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: 3

From Novice to Expert: Analysis of Token Level Effects in a Longitudinal Eye Tracking Study 从新手到专家:纵向眼动追踪研究中的标记水平效应分析

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00025

N. A. Madi, Cole S. Peterson, Bonita Sharif, J. Maletic

引用次数: 9

Understanding Execution Environment of File-Manipulation Scripts by Extracting Pre-Conditions 通过提取前提条件了解文件操作脚本的执行环境

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00048

Rodney Rodriguez, Xiaoyin Wang

引用次数: 2

Automated Comment Update: How Far are We? 自动评论更新:我们还有多远?

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00013

Bo Lin, Shangwen Wang, Kui Liu, Xiaoguang Mao, Tegawendé F. Bissyandé

{"title":"Automated Comment Update: How Far are We?","authors":"Bo Lin, Shangwen Wang, Kui Liu, Xiaoguang Mao, Tegawendé F. Bissyandé","doi":"10.1109/ICPC52881.2021.00013","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00013","url":null,"abstract":"Code comments are key to program comprehension. When they are not consistent with the code, maintenance is hindered. Yet developers often forget to update comments along with their code evolution. With recent advances in neural machine translation, the research community is contemplating novel approaches for automatically generating up-to-date comments following code changes. CUP is such an example state-of-the-art approach whose promising performance remains however to be comprehensively assessed. Our study contributes to the literature by performing an in-depth analysis on the effectiveness of CUP. Our analysis revealed that the overall effectiveness of CUP is largely contributed by its success on updating comments via a single token change (96.6%). Several update failures occur when CUP ignores some code change information (10.4%) or when it is otherwise misled by additional information (12.8%). To put in perspective the achievements of CUP, we implement HEBCUP, a straightforward heuristic-based approach for code comment update. Building on our observations on CUP successful and failure cases, we design heuristics for focusing the update on the changed code and for performing token-level comment update. HebCup is shown to outperform CUP in terms of Accuracy by more than 60% while being over three orders of magnitude (i.e., 1700 times) faster. Further empirical analysis confirms that the HebCup does not even overfit to the empirical analysis set. Overall, with this study, we call for more research in deep learning based comment update towards achieving state-of-the-art performance that would be unreachable by other less sophisticated techniques.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127552159","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: 21

Automatic Extraction of Code Dependency in Virtual Reality Software 虚拟现实软件中代码依赖关系的自动提取

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00043

Jacinto Molina, Xue Qin, Xiaoyin Wang

引用次数: 7

Toward Automated Exploit Generation for Known Vulnerabilities in Open-Source Libraries 面向开源库中已知漏洞的自动漏洞生成

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) Pub Date : 2021-05-01 DOI: 10.1109/ICPC52881.2021.00046

Emanuele Iannone, D. D. Nucci, A. Sabetta, A. D. Lucia

{"title":"Toward Automated Exploit Generation for Known Vulnerabilities in Open-Source Libraries","authors":"Emanuele Iannone, D. D. Nucci, A. Sabetta, A. D. Lucia","doi":"10.1109/ICPC52881.2021.00046","DOIUrl":"https://doi.org/10.1109/ICPC52881.2021.00046","url":null,"abstract":"Modern software applications, including commercial ones, extensively use Open-Source Software (OSS) components, accounting for 90% of software products on the market. This has serious security implications, mainly because developers rely on non-updated versions of libraries affected by software vulnerabilities. Several tools have been developed to help developers detect these vulnerable libraries and assess and mitigate their impact. The most advanced tools apply sophisticated reachability analyses to achieve high accuracy; however, they need additional data (in particular, concrete execution traces, such as those obtained by running a test suite) that is not always readily available.In this work, we propose SIEGE, a novel automatic exploit generation approach based on genetic algorithms, which generates test cases that execute the methods in a library known to contain a vulnerability. These test cases represent precious, concrete evidence that the vulnerable code can indeed be reached; they are also useful for security researchers to better understand how the vulnerability could be exploited in practice. This technique has been implemented as an extension of EVOSUITE and applied on set of 11 vulnerabilities exhibited by widely used OSS JAVA libraries. Our initial findings show promising results that deserve to be assessed further in larger-scale empirical studies.","PeriodicalId":288873,"journal":{"name":"2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122514300","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