2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)最新文献

{"title":"Sirius: Static Program Repair with Dependence Graph-Based Systematic Edit Patterns","authors":"Kunihiro Noda, Haruki Yokoyama, S. Kikuchi","doi":"10.1109/ICSME52107.2021.00045","DOIUrl":"https://doi.org/10.1109/ICSME52107.2021.00045","url":null,"abstract":"Software development often involves systematic edits, similar but nonidentical changes to many code locations, that are error-prone and laborious for developers. Mining and learning such systematic edit patterns (SEPs) from past code changes enable us to detect and repair overlooked buggy code that requires systematic edits. A recent study presented a promising SEP mining technique that is based on program dependence graphs (PDGs), while traditional approaches leverage syntax-based representations. PDG-based SEPs are highly expressive and can capture more meaningful changes than syntax-based ones. The next challenge to tackle is to apply the same code changes as in PDG-based SEPs to other code locations; detection and repair of overlooked locations that require systematic edits. Existing program transformation techniques cannot well address this challenge because (1) they expect many structural code similarities that are not guaranteed in PDG-based SEPs or (2) they work on the basis of PDGs but are limited to specific domains (e.g., API migrations). We present in this paper a general-purpose program transformation algorithm for applying PDG-based SEPs. Our algorithm identifies a small transplantable structural subtree for each PDG node, thereby adapting code changes from PDG-based SEPs to other locations. We construct a program repair pipeline Sirius that incorporates the algorithm and automates the processes of mining SEPs, detecting overlooked code locations (bugs) that require systematic edits, and repairing them by applying SEPs. We evaluated the repair performance of Sirius with a corpus of open source software consisting of over 80 repositories. The results indicate that Sirius greatly outperformed the state-of-the-art technique for syntax-based SEPs. Sirius achieved a precision of 0.710, recall of 0.565, and F1-score of 0.630, while those of the state-of-the-art technique were 0.470, 0.141, and 0.216, respectively.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116212619","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}

{"title":"Towards Just-Enough Documentation for Agile Effort Estimation: What Information Should Be Documented?","authors":"Jirat Pasuksmit, Patanamon Thongtanunam, Shanika A. Karunasekera","doi":"10.26226/morressier.613b5418842293c031b5b5d8","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b5d8","url":null,"abstract":"Effort estimation is an integral part of activities planning in Agile iterative development. An Agile team estimates the effort of a task based on the available information which is usually conveyed through documentation. However, as documentation has a lower priority in Agile, little is known about how documentation effort can be optimized while achieving accurate estimation. Hence, to help practitioners achieve just-enough documentation for effort estimation, we investigated the different types of documented information that practitioners considered useful for effort estimation. We conducted a survey study with 121 Agile practitioners across 25 countries. Our survey results showed that (1) despite the lower priority of documentation in Agile practices, 98% of the respondents considered documented information moderately to extremely important when estimating effort, (2) 73% of them reported that they would re-estimate a task when the documented information was changed, and (3) functional requirements, user stories, definition of done, UI wireframes, acceptance criteria, and task dependencies were ranked as the most useful types of documented information for effort estimation. Nevertheless, many respondents reported that these useful types of documented information were occasionally changing or missing. Based on our study results, we provide recommendations for agile practitioners on how effort estimation can be improved by focusing on just-enough documentation.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121452039","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}

{"title":"Design Smells in Deep Learning Programs: An Empirical Study","authors":"Amin Nikanjam, Foutse Khomh","doi":"10.26226/morressier.613b5418842293c031b5b61d","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b61d","url":null,"abstract":"Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural network (DNN) and then training it on a dataset. This process requires that developers make multiple architectural (e.g., type, size, number, and order of layers) and configuration (e.g., optimizer, regularization methods, and activation functions) choices that affect the quality of the DL models, and consequently software quality. An under-specified or poorly-designed DL model may train successfully but is likely to perform poorly when deployed in production. Design smells in DL programs are poor design and-or configuration decisions taken during the development of DL components, that are likely to have a negative impact on the performance (i.e., prediction accuracy) and then quality of DL based software systems. In this paper, we present a catalogue of 8 design smells for a popular DL architecture, namely deep Feedforward Neural Networks which is widely employed in industrial applications. The design smells were identified through a review of the existing literature on DL design and a manual inspection of 659 DL programs with performance issues and design inefficiencies. The smells are specified by describing their context, consequences, and recommended refactorings. To provide empirical evidence on the relevance and perceived impact of the proposed design smells, we conducted a survey with 81 DL developers. In general, the developers perceived the proposed design smells as reflective of design or implementation problems, with agreement levels varying between 47% and 68%.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"11 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120843370","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}

{"title":"Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data","authors":"Pooya Rostami Mazrae, M. Izadi, A. Heydarnoori","doi":"10.26226/morressier.613b5419842293c031b5b63b","DOIUrl":"https://doi.org/10.26226/morressier.613b5419842293c031b5b63b","url":null,"abstract":"An issue report documents the discussions around required changes in issue-tracking systems, while a commit contains the change itself in the version control systems. Recovering links between issues and commits can facilitate many software evolution tasks such as bug localization, defect prediction, software quality measurement, and software documentation. A previous study on over half a million issues from GitHub reports only about 42.2% of issues are manually linked by developers to their pertinent commits. Automating the linking of commit-issue pairs can contribute to the improvement of the said tasks. By far, current state-of-the-art approaches for automated commit-issue linking suffer from low precision, leading to unreliable results, sometimes to the point that imposes human supervision on the predicted links. The low performance gets even more severe when there is a lack of textual information in either commits or issues. Current approaches are also proven computationally expensive. We propose Hybrid-Linker, an enhanced approach that overcomes such limitations by exploiting two information channels; (1) a non-textual-based component that operates on non-textual, automatically recorded information of the commit-issue pairs to predict a link, and (2) a textual-based one which does the same using textual information of the commit-issue pairs. Then, combining the results from the two classifiers, Hybrid-Linker makes the final prediction. Thus, every time one component falls short in predicting a link, the other component fills the gap and improves the results. We evaluate Hybrid-Linker against competing approaches, namely FRLink and DeepLink on a dataset of 12 projects. Hybrid-Linker achieves 90.1%, 87.8%, and 88.9% based on recall, precision, and F-measure, respectively. It also outperforms FRLink and DeepLink by 31.3%, and 41.3%, regarding the F-measure. Moreover, the proposed approach exhibits extensive improvements in terms of performance as well. Finally, our source code and data are publicly available.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122865897","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}

{"title":"Multimodal Representation for Neural Code Search","authors":"Jian Gu, Zimin Chen, Monperrus Martin","doi":"10.1109/ICSME52107.2021.00049","DOIUrl":"https://doi.org/10.1109/ICSME52107.2021.00049","url":null,"abstract":"Semantic code search is about finding semantically relevant code snippets for a given natural language query. In the state-of-the-art approaches, the semantic similarity between code and query is quantified as the distance of their representation in the shared vector space. In this paper, to improve the vector space, we introduce tree-serialization methods on a simplified form of AST and build the multimodal representation for the code data. We conduct extensive experiments using a single corpus that is large-scale and multi-language: CodeSearchNet. Our results show that both our tree-serialized representations and multimodal learning model improve the performance of code search. Last, we define intuitive quantification metrics oriented to the completeness of semantic and syntactic information of the code data, to help understand the experimental findings.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116475965","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}

{"title":"Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models","authors":"Jingxuan Li, Rui Huang, Wei Li, Kai-Lang Yao, Weiguo Tan","doi":"10.26226/morressier.613b5419842293c031b5b638","DOIUrl":"https://doi.org/10.26226/morressier.613b5419842293c031b5b638","url":null,"abstract":"Code completion is widely used by software developers to provide coding suggestions given a partially written code snippet. Apart from the traditional code completion methods, which only support single token completion at minimal positions, recent studies show the ability to provide longer code completion at more flexible positions. However, such frequently triggered and longer completion results reduce the overall precision as they generate more invalid results. Moreover, different studies are mostly incompatible with each other. Thus, it is vital to develop an ensemble framework that can combine results from multiple models to draw merits and offset defects of each model. This paper conducts a coding simulation to collect data from code context and different code completion models and then apply the data in two tasks. First, we introduce an acceptance model which can dynamically control whether to display completion results to the developer. It uses simulation features to predict whether correct results exist in the output of these models. Our best model reduces the percentage of false-positive completion from 55.09% to 17.44%. Second, we design a fusion ranking scheme that can automatically identify the priority of the completion results and reorder the candidates from multiple code completion models. This scheme is flexible in dealing with various models, regardless of the type or the length of their completion results. We integrate this ranking scheme with two frequency models and a GPT-2 styled language model, along with the acceptance model to yield 27.80% and 37.64% increase in TOP1 and TOP5 accuracy, respectively. In addition, we propose a new code completion evaluation metric, Benefit-Cost Ratio(BCR), taking into account the benefit of keystrokes saving and hidden cost of completion list browsing, which is closer to real coder experience scenario.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124991287","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}

{"title":"CAT: Change-focused Android GUI Testing","authors":"Chao Peng, A. Rajan","doi":"10.26226/morressier.613b5418842293c031b5b5ec","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b5ec","url":null,"abstract":"Android Apps are frequently updated, every couple of weeks, to keep up with changing user, hardware and business demands. Correctness of App updates is checked through extensive testing. Recent research has proposed tools for automated GUI event generation in Android Apps. These techniques, however, are not efficient at checking App updates as the generated GUI events do not prioritise updates, and instead explore other App behaviours. We address this need in this paper with CAT (Change-focused Android GUI Testing). For App updates, at the source code or GUI level, CAT performs change impact analysis to identify GUI elements affected by the update. CAT then generates GUI event sequences to interact with the affected GUI elements. Our empirical evaluations using 21 publicly available open source and 2 commercial Android Apps demonstrate that CAT is able to automatically identify GUI elements affected by App updates, generate and execute GUI event sequences focusing on change-affected GUI elements. Comparison with two popular GUI event generation tools, DroidBot and DroidMate, revealed that CAT was more effective at interacting with the change-affected GUI elements. Finally, CAT was able to detect previously unknown change-related bugs in two open source Apps. Developers of the commercial Apps found CAT was more effective than their in-house GUI testing tool in interacting with changed elements and faster at detecting seeded bugs.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121670010","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}

{"title":"Task-Oriented API Usage Examples Prompting Powered By Programming Task Knowledge Graph","authors":"Jiamou Sun, Zhenchang Xing, Xin Peng, Xiwei Xu, Liming Zhu","doi":"10.26226/morressier.613b5418842293c031b5b5eb","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b5eb","url":null,"abstract":"Programming tutorials demonstrate programming tasks with code examples. However, our study of Stack Overflow questions reveals the low utilization of high-quality programming tutorials, which is caused task description mismatch and code information overload. Neither document search nor recently proposed activity-centric search can address these two barriers. In this work, we enrich the programming task knowledge graph with actions extracted from comments in code examples and more forms of activity sentences. To overcome the task description mismatch problem, we use code matching based task search method to find relevant programming tasks and code examples to the code under development. We integrate our knowledge graph and task search method in the IDE, and develop an observe-push based tool to prompt developers with API usage examples in explicit task contexts. To alleviate the code information overload problem, our tool highlights programming task and API information in the prompted tutorial excerpts and code examples based on the underlying knowledge graph. Our evaluation confirms the high quality of the constructed knowledge graph, and show that our code matching based task search can recommend effective code solutions to programming issues asked on Stack Overflow. Through an user study, we demonstrate that our tool is useful for assisting developers in finding and using relevant programming tutorials in their programming tasks.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114156945","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}

{"title":"Interactive Patch Filtering as Debugging Aid","authors":"Jingjing Liang, Ruyi Ji, Jiajun Jiang, Yiling Lou, Yingfei Xiong, Gang Huang","doi":"10.26226/morressier.613b5418842293c031b5b610","DOIUrl":"https://doi.org/10.26226/morressier.613b5418842293c031b5b610","url":null,"abstract":"It is widely recognized that patches generated by program repair tools have to be correct to be useful. However, it is fundamentally difficult to ensure the correctness of the patches. Many tools generate only the patches that are highly likely to be correct by taking conservative strategies which inevitably limit the recall of APR approaches. While the recall of APR can potentially be improved by relaxing the requirement on precision, more incorrect patches may also be generated. In this paper, we conjecture that reviewing incorrect patches also helps developers to understand the bug, and with proper tool support, reviewing incorrect patches would at least not reduce the repair performance. To evaluate this, we propose an interactive patch filtering approach to facilitate developers in the patch review process via effectively filtering out groups of incorrect patches. We implemented the approach as an Eclipse plugin, InPaFer, and evaluated the effectiveness and usefulness with a mixed-method evaluation. The results show that our approach improves the repair performance of developers, with 62.5% more successfully repaired bugs and 25.3% less debugging time. In particular, even if all generated patches are incorrect, the performance of developers would not be significantly reduced, and could still be improved. Our work provides a new way of thinking for the APR research.","PeriodicalId":205629,"journal":{"name":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128129652","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}