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Naturalness and Artifice of Code: Exploiting the Bi-Modality 代码的自然性与巧夺天工:利用双模态
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511915
Prem Devanbu
{"title":"Naturalness and Artifice of Code: Exploiting the Bi-Modality","authors":"Prem Devanbu","doi":"10.1145/3511430.3511915","DOIUrl":"https://doi.org/10.1145/3511430.3511915","url":null,"abstract":"While natural languages are rich in vocabulary and grammatical flexibility, most human are mundane and repetitive. This repetitiveness in natural language has led to great advances in statistical NLP methods. In our lab, we discovered (almost a decade ago) that, despite the considerable power and flexibility of programming languages, large software corpora are actually even more repetitive than NL Corpora. We also showed that this “naturalness” of code could be captured in language models, and exploited within software tools. This line of work has prospered, and been turbo-charged by the tremendous capacity and design flexibility of deep learning models. Numerous other creative and interesting applications of naturalness have ensued, from colleagues around the world, and several industrial applications have emerged. Recently, we have been studying the consequences and opportunities arising from the observation that Software is bimodal: it’s written not only to be run on machines, but also read by humans; this makes software amenable to both algorithmic analysis, and statistical prediction. Bimodality allows new ways of training machine learning models, new ways of designing analysis algorithms, and new ways to understand the practice of programming. In this talk, I will begin with a backgrounder on ”Naturalness” studies, and the promise of bimodality.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114901368","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
Automating Software Engineering with Machine Learning 用机器学习自动化软件工程
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511432
Aditya Kanade
{"title":"Automating Software Engineering with Machine Learning","authors":"Aditya Kanade","doi":"10.1145/3511430.3511432","DOIUrl":"https://doi.org/10.1145/3511430.3511432","url":null,"abstract":"Software plays a crucial role in our everyday lives. The scarcity of skilled software engineers has become a bottleneck in delivering better software at scale. Can we automate software engineering to help improve developer productivity and software quality? Can we take advantage of massive codebases to learn about building correct and scalable software? In this talk, I will present some recent advances in automated software engineering using machine learning. Along the way, I will relate the data-driven techniques to traditional, algorithmic program analysis techniques. I will discuss representative deep learning methods to analyze and synthesize source code. Even though we are witnessing exciting new advances in machine learning for software engineering, we shall reflect on what challenges remain and the way forward.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116151386","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
Classifying Toggles-smells and Investigating Development Effort 分类切换气味并调查开发工作
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511461
Harika Sugnanam, Md Tajmilur Rahman
{"title":"Classifying Toggles-smells and Investigating Development Effort","authors":"Harika Sugnanam, Md Tajmilur Rahman","doi":"10.1145/3511430.3511461","DOIUrl":"https://doi.org/10.1145/3511430.3511461","url":null,"abstract":"Companies are moving towards rapid release to deliver features as quickly as possible using Feature Toggles. Feature toggle is a variable that controls the state of a feature allowing unfinished code into the trunk. However, Maintaining the feature toggles needs a great effort, otherwise, it may lead to technical debt. Toggles may turn into code smells since they can be used in many ways if there is no standard of usage. We are calling such standard-less use of feature-toggles as “Toggle Smell’’. We classify different uses of toggle smells, and then we measure how much effort the code files are consuming to develop features, and maintain the toggles in each component. Our quantitative analysis on the Chromium open-source project finds that there are 3.1K toggles in 38 components and of the six different types of toggle usage, we classify three different toggle smells. The other types of usage will be analyzed in a future work. Three classification models predict the development effort in files as “High’’, “Medium’’, and “Low’’ with a similar accuracy of 95.x%.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129212335","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
A Report on Tutorials and Tech-Briefings co-located with ISEC 2022 关于与ISEC 2022共同定位的教程和技术简报的报告
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511464
M. Nagappan, Pavan Kumar Chittimalli
{"title":"A Report on Tutorials and Tech-Briefings co-located with ISEC 2022","authors":"M. Nagappan, Pavan Kumar Chittimalli","doi":"10.1145/3511430.3511464","DOIUrl":"https://doi.org/10.1145/3511430.3511464","url":null,"abstract":"This is a short report on the Tutorials and Tech Briefings session of the 15th Innovations in Software Engineering (ISEC 2022) conference held on 24-26th February 2022 in DA-IICT Gandhinagar, India. The tutorials and tech briefings at ISEC have been popular with the participants because they offer a gentle and friendly introduction to cutting edge topics and research at the frontiers of the discipline of software engineering. This year seven submissions were selected (2 Tech Briefings + 4 Tutorials) for presentation to reflect the current interests and directions of the field of software engineering.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127912251","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
Handling Memory-Intensive Operations in Symbolic Execution 处理符号执行中的内存密集型操作
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511453
Luca Borzacchiello, Emilio Coppa, C. Demetrescu
{"title":"Handling Memory-Intensive Operations in Symbolic Execution","authors":"Luca Borzacchiello, Emilio Coppa, C. Demetrescu","doi":"10.1145/3511430.3511453","DOIUrl":"https://doi.org/10.1145/3511430.3511453","url":null,"abstract":"Symbolic execution is a popular software testing technique that can help developers identify complex bugs in real-world applications. Unfortunately, symbolic execution may struggle at analyzing programs containing memory-intensive operations, such as memcpy and memset, whenever these operations are carried out over memory blocks whose size or address is symbolic, i.e., input-dependent. In this paper, we devise MInt, a memory model for symbolic execution that can support reasoning over such operations. The key new idea behind our proposal is to make the memory model aware of these memory-intensive operations, deferring any symbolic reasoning on their effects to the time where the program actually manipulates the symbolic data affected by these operations. We show that a preliminary implementation of MInt based on the symbolic framework angr can effectively analyze applications taken from the DARPA Cyber Grand Challenge.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830814","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
Evaluation of spectrum based fault localization tools 基于频谱的故障定位工具评价
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511470
Archana, Ashutosh Agarwal
{"title":"Evaluation of spectrum based fault localization tools","authors":"Archana, Ashutosh Agarwal","doi":"10.1145/3511430.3511470","DOIUrl":"https://doi.org/10.1145/3511430.3511470","url":null,"abstract":"Software Fault localization (SFL) is the first step in any program debugging process. For more than three decades, researchers have aggressively studied, evaluated, and proposed numerous automatic SFL techniques spanning across various families of methods such as spectrum-based, slice-based, mutation-based, etc. Another facet contributed by researchers is the practical implementation of the above techniques in the form of open-source tools, IDE plugins, extensions, etc. Examples include (but are not limited to) GZoltzar, Jaguar, and iFL4Eclipse. Previous research has established the metrics and threshold values for the adoption of SFL techniques in real-life software development. Several attempts have been made to evaluate automatic fault repair tools, and Information Retrieval (IR) based fault localization tools. Whilst Spectrum Based Fault Localization (SBFL) remains the most contributed family of SFL methods, no studies have been found which evaluate the existing SBFL tools. This paper presents a comparative theoretical assessment of selected SBFL tools by understanding the developmental dynamics involved in implementing them and establishing results that would guide the same in the future. Our research steps can briefly be summarized as systematic collection and filtering of SBFL tools and their research papers, developing a historical timeline for the same, and comparative theoretical analysis through the lens of software engineering. We theoretically determined that there is a lack of rigorous testing for the scalability and correctness of SBFL tools. While some tools are extensible with respect to the underlying algorithm used for computation, none provide flexibility in choosing the coverage collection framework. While Open-source tools have been more successful, there is a general lack in maintenance and development post initial publication. A natural progression of this work is a large-scale empirical assessment of the SBFL tools.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131204130","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
Designing, Developing and Deploying an Enterprise Scale Network Monitoring System 企业规模网络监控系统的设计、开发与部署
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511446
A. Basu, Rishi Singh, Chenyang Yu, Amarjeet Prasad, Kunal Banerjee
{"title":"Designing, Developing and Deploying an Enterprise Scale Network Monitoring System","authors":"A. Basu, Rishi Singh, Chenyang Yu, Amarjeet Prasad, Kunal Banerjee","doi":"10.1145/3511430.3511446","DOIUrl":"https://doi.org/10.1145/3511430.3511446","url":null,"abstract":"Walmart carries out its retail business across 27 countries both in the form of brick-and-mortar (∼ 11,500 stores and clubs) and e-commerce. To ensure smooth customer experience across the globe, we need to monitor the health of all devices ranging from networking hardware, storage spaces to compute servers spread across geographies all the time. Specifically, we need to monitor which device is facing as issue, when did this happen and what kind of alert does it call for. Swift remediation is carried out in a pro-active manner, i.e., before a device fails, and sometimes in re-active manner, i.e., after a device has failed. Tackling this challenge at an enterprise scale requires various technologies working together in a seamless manner. In this work, we give an insight about how the problem of network monitoring is handled at Walmart and elaborate on the design decisions taken.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131205960","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
Identifying Extract Method Refactorings 识别提取方法重构
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511435
Omkarendra Tiwari, R. Joshi
{"title":"Identifying Extract Method Refactorings","authors":"Omkarendra Tiwari, R. Joshi","doi":"10.1145/3511430.3511435","DOIUrl":"https://doi.org/10.1145/3511430.3511435","url":null,"abstract":"Extract method refactoring identifies and extracts a set of statements implementing a specific functionality within a method. Its application enhances the structure of code and provides improved readability and reusability. This paper introduces Segmentation, a new approach for identifying extract method opportunities focusing on achieving higher performance with fewer suggestions. Evaluation of the approach includes six case studies from the open-source domain, and performance is compared against two state-of-the-art approaches. The findings suggest that Segmentation provides improved precision and F measure over both the approaches. Further, improved performance is reflected over long methods too.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132356412","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
A Report on the Fifth Workshop on Software Engineering Education (SEED 2022) 第五届软件工程教育研讨会(SEED 2022)报告
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511467
Kuldeep Kumar, Bharti Suri, Bimlesh Wadhwa
{"title":"A Report on the Fifth Workshop on Software Engineering Education (SEED 2022)","authors":"Kuldeep Kumar, Bharti Suri, Bimlesh Wadhwa","doi":"10.1145/3511430.3511467","DOIUrl":"https://doi.org/10.1145/3511430.3511467","url":null,"abstract":"The 5th International Workshop on Software Engineering Education (SEED 2022), co-located with the 15th Innovations in Software Engineering Conference (ISEC 2022), aims to provide a unique forum to bring together researchers, educators, students, and practitioners to report on their experiences and their ongoing efforts in meeting the recent demands of remote teaching and learning in Software Engineering. The theme of SEED 2022 is Software Engineering Education amid a global pandemic - How can software engineering teaching meet the challenge of the sudden shift to online education triggered by the COVID-19 pandemic? Strategies for project-based learning, hybrid learning, blended learning, use of tools in teaching and learning are specifically targeted in this workshop. Further, it aims to provide a unique opportunity to Software Engineering educators and practitioners to come together and build collaborations for Software Engineering education research and practice.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129652016","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
A Report on the First Workshop on Knowledge Guided AI-Native Adaptive Enterprise 第一届知识引导人工智能自适应企业研讨会报告
15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511465
V. Kulkarni
{"title":"A Report on the First Workshop on Knowledge Guided AI-Native Adaptive Enterprise","authors":"V. Kulkarni","doi":"10.1145/3511430.3511465","DOIUrl":"https://doi.org/10.1145/3511430.3511465","url":null,"abstract":"Future enterprise will be a hyperconnected ecosystem that needs to deliver stated goals in a dynamic uncertain environment where the changes (even in goals) cannot be deduced upfront. Enterprise software is expected to play a principle role in its growth story. This calls for enterprise and its software to be capable of continuous adaptation in the face of uncertainty. Will innovative integration of proven ideas from modelling and simulation, AI, control theory and automated software engineering lead to a pragmatic solution?","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977947","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
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