2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)最新文献_第5页

DeepVisual: A Visual Programming Tool for Deep Learning Systems DeepVisual:深度学习系统的可视化编程工具

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-05-01 DOI: 10.1109/ICPC.2019.00028

Chao Xie, Hua Qi, Lei Ma, Jianjun Zhao

{"title":"DeepVisual: A Visual Programming Tool for Deep Learning Systems","authors":"Chao Xie, Hua Qi, Lei Ma, Jianjun Zhao","doi":"10.1109/ICPC.2019.00028","DOIUrl":"https://doi.org/10.1109/ICPC.2019.00028","url":null,"abstract":"As deep learning (DL) opens the way to many technological innovations in a wild range of fields, more and more researchers and developers from diverse domains start to take advantage of DLs. In many circumstances, a developer leverages a DL framework and programs the training software in the form of source code (e.g., Python, Java). However, not all of the developers across domains are skilled at programming. It is highly desirable to provide a way so that a developer could focus on how to design and optimize their DL systems instead of spending too much time on programming. To simplify the programming process towards saving time and effort especially for beginners, we propose and implement DeepVisual, a visual programming tool for the design and development of DL systems. DeepVisual represents each layer of a neural network as a component. A user can drag-and-drop components to design and build a DL model, after which the training code is automatically generated. Moreover, DeepVisual supports to extract the neural network architecture on the given source code as input. We implement DeepVisual as a PyCharm plugin and demonstrate its usefulness on two typical use cases.","PeriodicalId":6853,"journal":{"name":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","volume":"80 1","pages":"130-134"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88425263","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

Comparing Heuristic and Machine Learning Approaches for Metric-Based Code Smell Detection 基于度量的代码气味检测的启发式方法和机器学习方法的比较

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-05-01 DOI: 10.1109/ICPC.2019.00023

Fabiano Pecorelli, Fabio Palomba, D. D. Nucci, A. D. Lucia

{"title":"Comparing Heuristic and Machine Learning Approaches for Metric-Based Code Smell Detection","authors":"Fabiano Pecorelli, Fabio Palomba, D. D. Nucci, A. D. Lucia","doi":"10.1109/ICPC.2019.00023","DOIUrl":"https://doi.org/10.1109/ICPC.2019.00023","url":null,"abstract":"Code smells represent poor implementation choices performed by developers when enhancing source code. Their negative impact on source code maintainability and comprehensibility has been widely shown in the past and several techniques to automatically detect them have been devised. Most of these techniques are based on heuristics, namely they compute a set of code metrics and combine them by creating detection rules; while they have a reasonable accuracy, a recent trend is represented by the use of machine learning where code metrics are used as predictors of the smelliness of code artefacts. Despite the recent advances in the field, there is still a noticeable lack of knowledge of whether machine learning can actually be more accurate than traditional heuristic-based approaches. To fill this gap, in this paper we propose a large-scale study to empirically compare the performance of heuristic-based and machine-learning-based techniques for metric-based code smell detection. We consider five code smell types and compare machine learning models with DECOR, a state-of-the-art heuristic-based approach. Key findings emphasize the need of further research aimed at improving the effectiveness of both machine learning and heuristic approaches for code smell detection: while DECOR generally achieves better performance than a machine learning baseline, its precision is still too low to make it usable in practice.","PeriodicalId":6853,"journal":{"name":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","volume":"99 1","pages":"93-104"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78250981","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: 65

Recommending Differentiated Code to Support Smart Contract Update 推荐差异化代码支持智能合约更新

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-05-01 DOI: 10.1109/ICPC.2019.00045

Yuan Huang, Queping Kong, N. Jia, Xiangping Chen, Zibin Zheng

引用次数: 28

To CamelCase or under_score 到CamelCase或under_score

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-05-01 DOI: 10.1109/ICPC.2019.00035

D. Binkley, Marcia Davis, Dawn J Lawrie, Christopher Morrell

引用次数: 9

An Empirical Study on Practicality of Specification Mining Algorithms on a Real-World Application 规范挖掘算法在实际应用中的实用性实证研究

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-03-27 DOI: 10.1109/ICPC.2019.00020

Mohammad Jafar Mashhadi, H. Hemmati

引用次数: 5

Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledge 通过挖掘人群知识为编程任务推荐全面的解决方案

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-03-18 DOI: 10.1109/ICPC.2019.00054

Rodrigo F. Silva, C. Roy, M. M. Rahman, Kevin A. Schneider, K. V. R. Paixão, M. Maia

{"title":"Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledge","authors":"Rodrigo F. Silva, C. Roy, M. M. Rahman, Kevin A. Schneider, K. V. R. Paixão, M. Maia","doi":"10.1109/ICPC.2019.00054","DOIUrl":"https://doi.org/10.1109/ICPC.2019.00054","url":null,"abstract":"Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face two major problems. First, the search is impaired due to a lexical gap between their query (task description) and the information associated with the solution. Second, the retrieved solution may not be comprehensive, i.e., the code segment might miss a succinct explanation. These problems make the developers browse dozens of documents in order to synthesize an appropriate solution. To address these two problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) and provides a comprehensive solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations. Our proposed approach expands the task description with relevant API classes from Stack Overflow Q&A threads and then mitigates the lexical gap problems. Furthermore, we perform natural language processing on the top quality answers and then return such programming solutions containing code examples and code explanations unlike earlier studies. We evaluate our approach using 97 programming queries, of which 50% was used for training and 50% was used for testing, and show that it outperforms six baselines including the state-of-art by a statistically significant margin. Furthermore, our evaluation with 29 developers using 24 tasks (queries) confirms the superiority of CROKAGE over the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).","PeriodicalId":6853,"journal":{"name":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","volume":"7 1","pages":"358-368"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81927155","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: 29

A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors 轻量级生物传感器对代码理解和专业知识的复制研究

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-03-08 DOI: 10.1109/ICPC.2019.00050

D. Fucci, Daniela Girardi, Nicole Novielli, L. Quaranta, F. Lanubile

{"title":"A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors","authors":"D. Fucci, Daniela Girardi, Nicole Novielli, L. Quaranta, F. Lanubile","doi":"10.1109/ICPC.2019.00050","DOIUrl":"https://doi.org/10.1109/ICPC.2019.00050","url":null,"abstract":"Code comprehension has been recently investigated from physiological and cognitive perspectives using medical imaging devices. Floyd et al. (i.e., the original study) used fMRI to classify the type of comprehension tasks performed by developers and relate their results to their expertise. We replicate the original study using lightweight biometrics sensors. Our study participants—28 undergrads in computer science—performed comprehension tasks on source code and natural language prose. We developed machine learning models to automatically identify what kind of tasks developers are working on leveraging their brain-, heart-, and skin-related signals. The best improvement over the original study performance is achieved using solely the heart signal obtained through a single device (BAC 87%vs. 79.1%). Differently from the original study, we did not observe a correlation between the participants' expertise and the classifier performance (τ= 0.16, p= 0.31). Our findings show that lightweight biometric sensors can be used to accurately recognize comprehension opening interesting scenarios for research and practice.","PeriodicalId":6853,"journal":{"name":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","volume":"71 1","pages":"311-322"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83338113","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: 25

Replication Can Improve Prior Results: A GitHub Study of Pull Request Acceptance 复制可以改善先前的结果:对拉请求接受的GitHub研究

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2019-02-09 DOI: 10.1109/ICPC.2019.00037

Di Chen, Kathryn T. Stolee, T. Menzies

{"title":"Replication Can Improve Prior Results: A GitHub Study of Pull Request Acceptance","authors":"Di Chen, Kathryn T. Stolee, T. Menzies","doi":"10.1109/ICPC.2019.00037","DOIUrl":"https://doi.org/10.1109/ICPC.2019.00037","url":null,"abstract":"Crowdsourcing and data mining can be used to effectively reduce the effort associated with the partial replication and enhancement of qualitative studies. For example, in a primary study, other researchers explored factors influencing the fate of GitHub pull requests using an extensive qualitative analysis of 20 pull requests. Guided by their findings, we mapped some of their qualitative insights onto quantitative questions. To determine how well their findings generalize, we collected much more data (170 additional pull requests from 142 GitHub projects). Using crowdsourcing, that data was augmented with subjective qualitative human opinions about how pull requests extended the original issue. The crowd's answers were then combined with quantitative features and, using data mining, used to build a predictor for whether code would be merged. That predictor was far more accurate than the one built from the primary study's qualitative factors (F1=90 vs 68%), illustrating the value of a mixed-methods approach and replication to improve prior results. To test the generality of this approach, the next step in future work is to conduct other studies that extend qualitative studies with crowdsourcing and data mining.","PeriodicalId":6853,"journal":{"name":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","volume":"3 1","pages":"179-190"},"PeriodicalIF":0.0,"publicationDate":"2019-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72944187","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

Repeatedly-executed-method viewer for efficient visualization of execution paths and states in Java 重复执行的方法查看器，用于在Java中有效地可视化执行路径和状态

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2014-06-02 DOI: 10.1145/2597008.2597803

T. Matsumura, T. Ishio, Yu Kashima, Katsuro Inoue

引用次数: 15

On the effect of code regularity on comprehension 论代码规则性对理解的影响

2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC) Pub Date : 2014-06-02 DOI: 10.1145/2597008.2597140

Ahmad Jbara, D. Feitelson

{"title":"On the effect of code regularity on comprehension","authors":"Ahmad Jbara, D. Feitelson","doi":"10.1145/2597008.2597140","DOIUrl":"https://doi.org/10.1145/2597008.2597140","url":null,"abstract":"It is naturally easier to comprehend simple code relative to complicated code. Regrettably, there is little agreement on how to effectively measure code complexity. As a result simple generalpurpose metrics are often used, such as lines of code (LOC), Mc- Cabe’s cyclomatic complexity (MCC), and Halstead’s metrics. But such metrics just count syntactic features, and ignore details of the code’s global structure, which may also have an effect on understandability. In particular, we suggest that code regularity—where the same structures are repeated time after time—may significantly reduce complexity, because once one figures out the basic repeated element it is easier to understand additional instances. We demonstrate this by controlled experiments where subjects perform cognitive tasks on different versions of the same basic function. The results indicate that versions with significant regularity lead to better comprehension, while taking similar time, despite being longer and having higherMCC. These results indicate that regularity is another attribute of code that should be taken into account in the context of studying the code’s complexity and comprehension. Moreover, the fact that regularity may compensate for LOC and MCC demonstrates that complexity cannot be decomposed into independently addable contributions by individual attributes.","PeriodicalId":6853,"journal":{"name":"2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)","volume":"28 1","pages":"189-200"},"PeriodicalIF":0.0,"publicationDate":"2014-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90584283","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: 35