软件产业与工程最新文献

{"title":"NL2Viz: natural language to visualization via constrained syntax-guided synthesis","authors":"Zhengkai Wu, Vu Le, A. Tiwari, Sumit Gulwani, Arjun Radhakrishna, Ivan Radicek, Gustavo Soares, Xinyu Wang, Zhenwen Li, Tao Xie","doi":"10.1145/3540250.3549140","DOIUrl":"https://doi.org/10.1145/3540250.3549140","url":null,"abstract":"Recent development in NL2CODE (Natural Language to Code) research allows end-users, especially novice programmers to create a concrete implementation of their ideas such as data visualization by providing natural language (NL) instructions. An NL2CODE system often fails to achieve its goal due to three major challenges: the user's words have contextual semantics, the user may not include all details needed for code generation, and the system results are imperfect and require further refinement. To address the aforementioned three challenges for NL to Visualization, we propose a new approach and its supporting tool named NL2VIZ with three salient features: (1) leveraging not only the user's NL input but also the data and program context that the NL query is upon, (2) using hard/soft constraints to reflect different confidence levels in the constraints retrieved from the user input and data/program context, and (3) providing support for result refinement and reuse. We implement NL2VIZ in the Jupyter Notebook environment and evaluate NL2VIZ on a real-world visualization benchmark and a public dataset to show the effectiveness of NL2VIZ. We also conduct a user study involving 6 data scientist professionals to demonstrate the usability of NL2VIZ, the readability of the generated code, and NL2VIZ's effectiveness in helping users generate desired visualizations effectively and efficiently.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79749289","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":"What motivates software practitioners to contribute to inner source?","authors":"Zhiyuan Wan, Xin Xia, Yun Zhang, David Lo, Daibing Zhou, Qiuyuan Chen, A. Hassan","doi":"10.1145/3540250.3549148","DOIUrl":"https://doi.org/10.1145/3540250.3549148","url":null,"abstract":"Software development organizations have adopted open source development practices to support or augment their software development processes, a phenomenon referred to as inner source. Given the rapid adoption of inner source, we wonder what motivates software practitioners to contribute to inner source projects. We followed a mixed-methods approach--a qualitative phase of interviews with 20 interviewees, followed by a quantitative phase of an exploratory survey with 124 respondents from 13 countries across four continents. Our study uncovers practitioners' motivation to contribute to inner source projects, as well as how the motivation differs from what motivates practitioners to participate in open source projects. We also investigate how software practitioners' motivation impacts their contribution level and continuance intention in inner source projects. Based on our findings, we outline directions for future research and provide recommendations for organizations and software practitioners.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89523866","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":"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"title":"Investigating and improving log parsing in practice","authors":"Ying Fu, Meng Yan, Jian Xu, Jianguo Li, Zhongxin Liu, Xiaohong Zhang, Dan Yang","doi":"10.1145/3540250.3558947","DOIUrl":"https://doi.org/10.1145/3540250.3558947","url":null,"abstract":"Logs are widely used for system behavior diagnosis by automatic log mining. Log parsing is an important data preprocessing step that converts semi-structured log messages into structured data as the feature input for log mining. Currently, many studies are devoted to proposing new log parsers. However, to the best of our knowledge, no previous study comprehensively investigates the effectiveness of log parsers in industrial practice. To investigate the effectiveness of the log parsers in industrial practice, in this paper, we conduct an empirical study on the effectiveness of six state-of-the-art log parsers on 10 microservice applications of Ant Group. Our empirical results highlight two challenges for log parsing in practice: 1) various separators. There are various separators in a log message, and the separators in different event templates or different applications are also various. Current log parsers cannot perform well because they do not consider various separators. 2) Various lengths due to nested objects. The log messages belonging to the same event template may also have various lengths due to nested objects. The log messages of 6 out of 10 microservice applications at Ant Group with various lengths due to nested objects. 4 out of 6 state-of-the-art log parsers cannot deal with various lengths due to nested objects. In this paper, we propose an improved log parser named Drain+ based on a state-of-the-art log parser Drain. Drain+ includes two innovative components to address the above two challenges: a statistical-based separators generation component, which generates separators automatically for log message splitting, and a candidate event template merging component, which merges the candidate event templates by a template similarity method. We evaluate the effectiveness of Drain+ on 10 microservice applications of Ant Group and 16 public datasets. The results show that Drain+ outperforms the six state-of-the-art log parsers on industrial applications and public datasets. Finally, we conclude the observations in the road ahead for log parsing to inspire other researchers and practitioners.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77372831","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":"Detecting Simulink compiler bugs via controllable zombie blocks mutation","authors":"Shikai Guo, He Jiang, Zhihao Xu, Xiaochen Li, Zhilei Ren, Zhide Zhou, Rong Chen","doi":"10.1145/3540250.3549159","DOIUrl":"https://doi.org/10.1145/3540250.3549159","url":null,"abstract":"As a popular Cyber-Physical System (CPS) development tool chain, MathWorks Simulink is widely used to prototype CPS models in safety-critical applications, e.g., aerospace and healthcare. It is crucial to ensure the correctness and reliability of Simulink compiler (i.e., the compiler module of Simulink) in practice since all CPS models depend on compilation. However, Simulink compiler testing is challenging due to millions of lines of source code and the lack of the complete formal language specification. Although several methods have been proposed to automatically test Simulink compiler, there still remains two challenges to be tackled, namely the limited variant space and the insufficient mutation diversity. To address these challenges, we propose COMBAT, a new differential testing method for Simulink compiler testing. COMBAT includes an EMI (Equivalence Modulo Input) mutation component and a diverse variant generation component. The EMI mutation component inserts assertion statements (e.g., If /While blocks) at arbitrary points of the seed CPS model. These statements break each insertion point into true and false branches. Then, COMBAT feeds all the data passed through the insertion point into the true branch to preserve the equivalence of CPS variants. In such a way, the body of the false branch could be viewed as a new variant space, thus addressing the first challenge. The diverse variant generation component uses Markov chain Monte Carlo optimization to sample the seed CPS model and generate complex mutations of long sequences of blocks in the variant space, thus addressing the second challenge. Experiments demonstrate that COMBAT significantly outperforms the state-of-the-art approaches in Simulink compiler testing. Within five months, COMBAT has reported 16 valid bugs for Simulink R2021b, of which 11 bugs have been confirmed as new bugs by MathWorks Support.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73407463","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":"SFLKit: a workbench for statistical fault localization","authors":"Marius Smytzek, A. Zeller","doi":"10.1145/3540250.3558915","DOIUrl":"https://doi.org/10.1145/3540250.3558915","url":null,"abstract":"Statistical fault localization aims at detecting execution features that correlate with failures, such as whether individual lines are part of the execution. We introduce SFLKit, an out-of-the-box workbench for statistical fault localization. The framework provides straightforward access to the fundamental concepts of statistical fault localization. It supports five predicate types, four coverage-inspired spectra, like lines, and 44 similarity coefficients, e.g., TARANTULA or OCHIAI, for statistical program analysis. SFLKit separates the execution of tests from the analysis of the results and is therefore independent of the used testing framework. It leverages program instrumentation to enable the logging of events and derives the predicates and spectra from these logs. This instrumentation allows for introducing multiple programming languages and the extension of new concepts in statistical fault localization. Currently, SFLKit supports the instrumentation of Python programs. It is highly configurable, requiring only the logging of the required events.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83236709","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":"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}

{"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":null,"pages":null},"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}