15th Innovations in Software Engineering Conference最新文献

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

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

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

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

引用次数: 2

Supporting Readability by Comprehending the Hierarchical Abstraction of a Software Project 通过理解软件项目的层次抽象来支持可读性

15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511441

Avijit Bhattacharjee, B. Roy, Kevin A. Schneider

{"title":"Supporting Readability by Comprehending the Hierarchical Abstraction of a Software Project","authors":"Avijit Bhattacharjee, B. Roy, Kevin A. Schneider","doi":"10.1145/3511430.3511441","DOIUrl":"https://doi.org/10.1145/3511430.3511441","url":null,"abstract":"Exploring the source code of a software system is a prevailing task that is frequently done by contributors to a system. Practitioners often use call graphs to aid in understanding the source code of an inadequately documented software system. Call graphs, when visualized, show caller and callee relationships between functions. A static call graph provides an overall structure of a software system and dynamic call graphs generated from dynamic execution logs can be used to trace program behaviour for a particular scenario. Unfortunately a call graph of an entire system can be very complicated and hard to understand. Hierarchically abstracting a call graph can be used to summarize an entire system’s structure and more easily comprehending function calls. In this work, we mine concepts from source code entities (functions) to generate a concept cluster tree with improved naming of cluster nodes to complement existing studies and facilitate more effective program comprehension for developers. We apply three different information retrieval techniques (TFIDF, LDA, and LSI) on function names and function name variants to label the nodes of a concept cluster tree generated by clustering execution paths. From our experiment in comparing automatic labelling with manual labeling by participants for 12 use cases, we found that among the techniques on average, TFIDF performs better with 64% matching. LDA and LSI had 37% and 23% matching respectively. In addition, using the words in function name variants performed at least 5% better in participant ratings for all three techniques on average for the use cases.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"114 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":"128115492","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 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

引用次数: 0

Identifying and Extracting Hierarchical Information from Business PDF Documents 从商业PDF文档中识别和提取层次信息

15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511440

Rohit Shere, Pavan Kumar Chittimalli, Ravindra Naik

{"title":"Identifying and Extracting Hierarchical Information from Business PDF Documents","authors":"Rohit Shere, Pavan Kumar Chittimalli, Ravindra Naik","doi":"10.1145/3511430.3511440","DOIUrl":"https://doi.org/10.1145/3511430.3511440","url":null,"abstract":"Portable Document Format (PDF) is a popular choice for a secure communication and persistence of business information and is a universally accepted format by businesses choosing to become digital. PDF provides multiple ways to make the information visually appealing and readable, and device independent rendering. To achieve this, PDF stores metadata with individual text characters, graphic components and other layout elements. Such atomic component wise meta-data makes machine processing of information in the PDF format very challenging; the challenge is further extended due to the difficulty of stitching together the original semantics from the componentized information. We propose a generic approach for extracting the hierarchy of the document structure while separating the content from header and footer, and extracting metadata associated with checkboxes to annotate the business information contained in PDF for tasks like mining specifications and rules from the document. Our prototype is able to process real-life, large PDF documents each running into roughly 400 pages, with nearly 95% of the extraction requiring no human intervention.","PeriodicalId":138760,"journal":{"name":"15th Innovations in Software Engineering Conference","volume":"12 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":"126777243","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

引用次数: 0

Enhancing Configurable Limitless Paths in Virtual Reality Environments 增强虚拟现实环境中的可配置无限路径

15th Innovations in Software Engineering Conference Pub Date : 2022-02-24 DOI: 10.1145/3511430.3511452

Raghav Mittal, Sai Anirudh Karre, Y. P. Gururaj, Y. R. Reddy

引用次数: 1