软件产业与工程最新文献_第8页

AgileCtrl: a self-adaptive framework for configuration tuning AgileCtrl:用于配置调优的自适应框架

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3549136

Shu Wang, Henry Hoffmann, Shan Lu

{"title":"AgileCtrl: a self-adaptive framework for configuration tuning","authors":"Shu Wang, Henry Hoffmann, Shan Lu","doi":"10.1145/3540250.3549136","DOIUrl":"https://doi.org/10.1145/3540250.3549136","url":null,"abstract":"Software systems increasingly expose performance-sensitive configuration parameters, or PerfConfs, to users. Unfortunately, the right settings of these PerfConfs are difficult to decide and often change at run time. To address this problem, prior research has proposed self-adaptive frameworks that automatically monitor the software’s behavior and dynamically tune configurations to provide the desired performance despite dynamic changes. However, these frameworks often require configuration themselves; sometimes explicitly in the form of additional parameters, sometimes implicitly in the form of training. This paper proposes a new framework, AgileCtrl, that eliminates the need of configuration for a large family of control-based self-adaptive frameworks. AgileCtrl’s key insight is to not just monitor the original software, but additionally to monitor its adaptations and reconfigure itself when its internal adaptation mechanisms are not meeting software requirements. We evaluate AgileCtrl by comparing against recent control-based approaches to self-adaptation that require user configuration. Across a number of case studies, we find AgileCtrl withstands model errors up to 106×, saves the system from performance oscillation and crashes, and improves the performance up to 53%. It also auto-adjusts improper performance goals while improving the performance by 50%.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87487617","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

JSIMutate: understanding performance results through mutations JSIMutate:通过突变理解性能结果

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3558930

Thomas Laurent, Paolo Arcaini, Catia Trubiani, Anthony Ventresque

引用次数: 0

AccessiText: automated detection of text accessibility issues in Android apps AccessiText:自动检测Android应用程序中的文本可访问性问题

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3549118

Abdulaziz Alshayban, S. Malek

{"title":"AccessiText: automated detection of text accessibility issues in Android apps","authors":"Abdulaziz Alshayban, S. Malek","doi":"10.1145/3540250.3549118","DOIUrl":"https://doi.org/10.1145/3540250.3549118","url":null,"abstract":"For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The growing reliance of users with disability on mobile apps to complete their day-to-day tasks further stresses the need for accessible software. Mobile operating systems, such as iOS and Android, provide various integrated assistive services to help individuals with disabilities perform tasks that could otherwise be difficult or not possible. However, for these assistive services to work correctly, developers have to support them in their app by following a set of best practices and accessibility guidelines. Text Scaling Assistive Service (TSAS) is utilized by people with low vision, to increase the text size and make apps accessible to them. However, the use of TSAS with incompatible apps can result in unexpected behavior introducing accessibility barriers to users. This paper presents approach, an automated testing technique for text accessibility issues arising from incompatibility between apps and TSAS. As a first step, we identify five different types of text accessibility by analyzing more than 600 candidate issues reported by users in (i) app reviews for Android and iOS, and (ii) Twitter data collected from public Twitter accounts. To automatically detect such issues, approach utilizes the UI screenshots and various metadata information extracted using dynamic analysis, and then applies various heuristics informed by the different types of text accessibility issues identified earlier. Evaluation of approach on 30 real-world Android apps corroborates its effectiveness by achieving 88.27% precision and 95.76% recall on average in detecting text accessibility issues.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81829143","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

Exploring the under-explored terrain of non-open source data for software engineering through the lens of federated learning 通过联邦学习的视角探索软件工程中未开发的非开源数据领域

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3560883

Shriram Shanbhag, S. Chimalakonda

{"title":"Exploring the under-explored terrain of non-open source data for software engineering through the lens of federated learning","authors":"Shriram Shanbhag, S. Chimalakonda","doi":"10.1145/3540250.3560883","DOIUrl":"https://doi.org/10.1145/3540250.3560883","url":null,"abstract":"The availability of open source projects on platforms like GitHub has led to the wide use of the artifacts from these projects in software engineering research. These publicly available artifacts have been used to train artificial intelligence models used in various empirical studies and the development of tools. However, these advancements have missed out on the artifacts from non-open source projects due to the unavailability of the data. A major cause for the unavailability of the data from non-open source repositories is the issue concerning data privacy. In this paper, we propose using federated learning to address the issue of data privacy to enable the use of data from non-open source to train AI models used in software engineering research. We believe that this can potentially enable industries to collaborate with software engineering researchers without concerns about privacy. We present the preliminary evaluation of the use of federated learning to train a classifier to label bug-fix commits from an existing study to demonstrate its feasibility. The federated approach achieved an F1 score of 0.83 compared to a score of 0.84 using the centralized approach. We also present our vision of the potential implications of the use of federated learning in software engineering research.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90469775","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

Workgraph: personal focus vs. interruption for engineers at Meta 工作图:Meta工程师的个人关注与干扰

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3558961

Yifen Chen, Peter C. Rigby, Yulin Chen, Kun Jiang, Nader Dehghani, Qianying Huang, Peter Cottle, Clayton Andrews, Noah Lee, Nachiappan Nagappan

{"title":"Workgraph: personal focus vs. interruption for engineers at Meta","authors":"Yifen Chen, Peter C. Rigby, Yulin Chen, Kun Jiang, Nader Dehghani, Qianying Huang, Peter Cottle, Clayton Andrews, Noah Lee, Nachiappan Nagappan","doi":"10.1145/3540250.3558961","DOIUrl":"https://doi.org/10.1145/3540250.3558961","url":null,"abstract":"All engineers dislike interruptions because it takes away from the deep focus time needed to write complex code. Our goal is to reduce unnecessary interruptions at . We first describe our Workgraph platform that logs how engineers use our internal work tools at . Using these anonymized logs, we create sessions. sessions are defined in opposition to interruption and are the amount of time until the engineer is interrupted by, for example, a work chat message. We describe descriptive statistics related to how long engineers are able to focus. We find that at Meta, Engineers have a total of 14.25 hours of personal-focus time per week. These numbers are comparable with those reported by other software firms. We then create a Random Forest model to understand which factors influence the median daily personal-focus time. We find that the more time an engineer spends in the IDE the longer their focus. We also find that the more central an engineer is in the social work network, the shorter their personal-focus time. Other factors such as role and domain/pillar have little impact on personal-focus at Meta. To help engineers achieve longer blocks of personal-focus and help them stay in flow, Meta developed the AutoFocus tool that blocks work chat notifications when an engineer is working on code for 12 minutes or longer. AutoFocus allows the sender to still force a work chat message using “@notify” ensuring that urgent messages still get through, but allowing the sender to reflect on the importance of the message. In a large experiment, we find that AutoFocus increases the amount of personal-focus time by 20.27%, and it has now been rolled out widely at Meta.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75920687","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

SFLKit: a workbench for statistical fault localization SFLKit:统计故障定位工作台

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3558915

Marius Smytzek, A. Zeller

引用次数: 2

Large-scale analysis of non-termination bugs in real-world OSS projects 对真实OSS项目中的非终止错误进行大规模分析

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3549129

X. Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li

{"title":"Large-scale analysis of non-termination bugs in real-world OSS projects","authors":"X. Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li","doi":"10.1145/3540250.3549129","DOIUrl":"https://doi.org/10.1145/3540250.3549129","url":null,"abstract":"Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing well-established benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study of non-termination bugs in real-world OSS projects. Specifically, we first devoted substantial manual efforts in collecting and analyzing 445 non-termination bugs from 3,142 GitHub commits and provided a systematic classifi-cation of the bugs based on their root causes. We constructed a new benchmark set characterizing the real-world bugs with simplified programs, including a non-termination dataset with 56 real and reproducible non-termination bugs and a termination dataset with 58 fixed programs. With the constructed benchmark, we evaluated five state-of-the-art termination analysis tools. The results show that the capabilities of the tested tools to make correct verdicts have obviously dropped compared with the existing benchmarks. Meanwhile, we identified the challenges and limitations that these tools face by analyzing the root causes of their unhandled bugs. Fi-nally, we summarized the challenges and future research directions for detecting non-termination bugs in real-world projects.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88550106","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: 2

Perfect is the enemy of test oracle 完美是测试oracle的敌人

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3549086

Ali Reza Ibrahimzada, Yigit Varli, Dilara Tekinoglu, Reyhaneh Jabbarvand

{"title":"Perfect is the enemy of test oracle","authors":"Ali Reza Ibrahimzada, Yigit Varli, Dilara Tekinoglu, Reyhaneh Jabbarvand","doi":"10.1145/3540250.3549086","DOIUrl":"https://doi.org/10.1145/3540250.3549086","url":null,"abstract":"Automation of test oracles is one of the most challenging facets of software testing, but remains comparatively less addressed compared to automated test input generation. Test oracles rely on a ground-truth that can distinguish between the correct and buggy behavior to determine whether a test fails (detects a bug) or passes. What makes the oracle problem challenging and undecidable is the assumption that the ground-truth should know the exact expected, correct, or buggy behavior. However, we argue that one can still build an accurate oracle without knowing the exact correct or buggy behavior, but how these two might differ. This paper presents , a learning-based approach that in the absence of test assertions or other types of oracle, can determine whether a unit test passes or fails on a given method under test (MUT). To build the ground-truth, jointly embeds unit tests and the implementation of MUTs into a unified vector space, in such a way that the neural representation of tests are similar to that of MUTs they pass on them, but dissimilar to MUTs they fail on them. The classifier built on top of this vector representation serves as the oracle to generate “fail” labels, when test inputs detect a bug in MUT or “pass” labels, otherwise. Our extensive experiments on applying to more than 5K unit tests from a diverse set of open-source Java projects show that the produced oracle is (1) effective in predicting the fail or pass labels, achieving an overall accuracy, precision, recall, and F1 measure of 93%, 86%, 94%, and 90%, (2) generalizable, predicting the labels for the unit test of projects that were not in training or validation set with negligible performance drop, and (3) efficient, detecting the existence of bugs in only 6.5 milliseconds on average. Moreover, by interpreting the neural model and looking at it beyond a closed-box solution, we confirm that the oracle is valid, i.e., it predicts the labels through learning relevant features.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78288312","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

Classifying edits to variability in source code 根据源代码的可变性对编辑进行分类

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3549108

P. M. Bittner, Christof Tinnes, Alexander Schultheiss, Sören Viegener, Timo Kehrer, Thomas Thüm

{"title":"Classifying edits to variability in source code","authors":"P. M. Bittner, Christof Tinnes, Alexander Schultheiss, Sören Viegener, Timo Kehrer, Thomas Thüm","doi":"10.1145/3540250.3549108","DOIUrl":"https://doi.org/10.1145/3540250.3549108","url":null,"abstract":"For highly configurable software systems, such as the Linux kernel, maintaining and evolving variability information along changes to source code poses a major challenge. While source code itself may be edited, also feature-to-code mappings may be introduced, removed, or changed. In practice, such edits are often conducted ad-hoc and without proper documentation. To support the maintenance and evolution of variability, it is desirable to understand the impact of each edit on the variability. We propose the first complete and unambiguous classification of edits to variability in source code by means of a catalog of edit classes. This catalog is based on a scheme that can be used to build classifications that are complete and unambiguous by construction. To this end, we introduce a complete and sound model for edits to variability. In about 21.5ms per commit, we validate the correctness and suitability of our classification by classifying each edit in 1.7 million commits in the change histories of 44 open-source software systems automatically. We are able to classify all edits with syntactically correct feature-to-code mappings and find that all our edit classes occur in practice.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75220349","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

An empirical investigation of missing data handling in cloud node failure prediction 云节点故障预测中缺失数据处理的实证研究

软件产业与工程 Pub Date : 2022-11-07 DOI: 10.1145/3540250.3558946

Minghua Ma, Yudong Liu, Yuang Tong, Haozhe Li, Pu Zhao, Yong Xu, Hongyu Zhang, Shilin He, Lu Wang, Yingnong Dang, S. Rajmohan, Qingwei Lin

{"title":"An empirical investigation of missing data handling in cloud node failure prediction","authors":"Minghua Ma, Yudong Liu, Yuang Tong, Haozhe Li, Pu Zhao, Yong Xu, Hongyu Zhang, Shilin He, Lu Wang, Yingnong Dang, S. Rajmohan, Qingwei Lin","doi":"10.1145/3540250.3558946","DOIUrl":"https://doi.org/10.1145/3540250.3558946","url":null,"abstract":"Cloud computing systems have become increasingly popular in recent years. A typical cloud system utilizes millions of computing nodes as the basic infrastructure. Node failure has been identified as one of the most prevalent causes of cloud system downtime. To improve the reliability of cloud systems, many previous studies collected monitoring metrics from nodes and built models to predict node failures before the failures happen. However, based on our experience with large-scale real-world cloud systems in Microsoft, we find that the task of predicting node failure is severely hampered by missing data. There is a large amount of missing data, and the online latest data utilized for prediction is even worse. As a result, the real-time performance of the node prediction model is limited. In this paper, we first characterize the missing data problem for node failure prediction. Then, we evaluate several existing data interpolation approaches, and find that node dimension interpolation approaches outperform time dimension ones and deep learning based interpolation is the best for early prediction. Our findings can help academics and engineers address the missing data problem in cloud node failure prediction and other data-driven software engineering scenarios.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"30 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82966995","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