2017 IEEE 25th International Requirements Engineering Conference (RE)最新文献_第7页

Legal Markup Generation in the Large: An Experience Report 大型法律标记生成:一份经验报告

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.10

Nicolas Sannier, Morayo Adedjouma, M. Sabetzadeh, L. Briand, J. Dann, Marc Hisette, Pascal Thill

{"title":"Legal Markup Generation in the Large: An Experience Report","authors":"Nicolas Sannier, Morayo Adedjouma, M. Sabetzadeh, L. Briand, J. Dann, Marc Hisette, Pascal Thill","doi":"10.1109/RE.2017.10","DOIUrl":"https://doi.org/10.1109/RE.2017.10","url":null,"abstract":"Legal markup (metadata) is an important prerequisite for the elaboration of legal requirements. Manually encoding legal texts into a markup representation is laborious, specially for large legal corpora amassed over decades and centuries. At the same time, automating the generation of markup in a fully accurate manner presents a challenge due to the flexibility of the natural-language content in legal texts and variations in how these texts are organized. Following an action research method, we successfully collaborated with the Government of Luxembourg in transitioning five major legislative codes from plain-text to a legal markup format. Our work focused on generating markup for the structural elements of the underlying codes. The technical basis for our work is an adaptation and enhancement of an academic markup generation tool developed in our prior research [1]. We reflect on the experience gained from applying automated markup generation at large scales. In particular, we elaborate the decisions we made in order to strike a cost-effective balance between automation and manual work for legal markup generation. We evaluate the quality of automatically-generated structural markup in real-world conditions and subject to the practical considerations of our collaborating partner.","PeriodicalId":176958,"journal":{"name":"2017 IEEE 25th International Requirements Engineering Conference (RE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134013365","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: 9

The Vision: Requirements Engineering in Society 愿景:社会中的需求工程

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.70

G. Ruhe, Maleknaz Nayebi, C. Ebert

引用次数: 19

RE Data Challenge: Requirements Identification with Word2Vec and TensorFlow RE数据挑战:用Word2Vec和TensorFlow识别需求

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.26

Alex Dekhtyar, Vivian Fong

引用次数: 30

A Data Purpose Case Study of Privacy Policies 隐私政策的数据目的案例研究

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.56

Jaspreet Bhatia, T. Breaux

引用次数: 15

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution 一只小鸟告诉我:挖掘推文的需求和软件进化

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.88

Emitzá Guzmán, M. Ibrahim, M. Glinz

{"title":"A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution","authors":"Emitzá Guzmán, M. Ibrahim, M. Glinz","doi":"10.1109/RE.2017.88","DOIUrl":"https://doi.org/10.1109/RE.2017.88","url":null,"abstract":"Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number---in the range of thousands per day for popular applications---a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.","PeriodicalId":176958,"journal":{"name":"2017 IEEE 25th International Requirements Engineering Conference (RE)","volume":"os-56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127720159","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: 95

Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning 使用监督机器学习自动分类功能和非功能需求

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.82

Zijad Kurtanović, W. Maalej

{"title":"Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning","authors":"Zijad Kurtanović, W. Maalej","doi":"10.1109/RE.2017.82","DOIUrl":"https://doi.org/10.1109/RE.2017.82","url":null,"abstract":"In this paper, we take up the second RE17 data challenge: the identification of requirements types using the \"Quality attributes (NFR)\" dataset provided. We studied how accurately we can automatically classify requirements as functional (FR) and non-functional (NFR) in the dataset with supervised machine learning. Furthermore, we assessed how accurately we can identify various types of NFRs, in particular usability, security, operational, and performance requirements. We developed and evaluated a supervised machine learning approach employing meta-data, lexical, and syntactical features. We employed under-and over-sampling strategies to handle the imbalanced classes in the dataset and cross-validated the classifiers using precision, recall, and F1 metrics in a series of experiments based on the Support Vector Machine classifier algorithm. We achieve a precision and recall up to ~92% for automatically identifying FRs and NFRs. For the identification of specific NFRs, we achieve the highest precision and recall for security and performance NFRs with ~92% precision and ~90% recall. We discuss the most discriminating features of FRs and NFRs as well as the sampling strategies used with an additional dataset and their impact on the classification accuracy.","PeriodicalId":176958,"journal":{"name":"2017 IEEE 25th International Requirements Engineering Conference (RE)","volume":"101 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133173795","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: 135

Deploying a Template and Pattern Library for Improved Reuse of Requirements Across Projects 部署模板和模式库以改进跨项目的需求重用

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.44

Robert Darimont, Wei Zhao, C. Ponsard, Arnaud Michot

引用次数: 11

A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements Through an Online Survey 通过在线调查评估某些写作要求规则相关性的案例研究

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.11

Maxime Warnier, A. Condamines

引用次数: 5

Panel: Context-Dependent Evaluation of Tools for NL RE Tasks: Recall vs. Precision, and Beyond 小组讨论:NL - RE任务工具的情境依赖评估:召回率与准确率，以及其他

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.64

D. Berry, J. Cleland-Huang, Alessio Ferrari, W. Maalej, J. Mylopoulos, D. Zowghi

引用次数: 11

Interview Review: Detecting Latent Ambiguities to Improve the Requirements Elicitation Process 面试回顾:发现潜在的歧义以改进需求引出过程

2017 IEEE 25th International Requirements Engineering Conference (RE) Pub Date : 2017-09-01 DOI: 10.1109/RE.2017.15

Alessio Ferrari, P. Spoletini, Beatrice Donati, D. Zowghi, S. Gnesi

{"title":"Interview Review: Detecting Latent Ambiguities to Improve the Requirements Elicitation Process","authors":"Alessio Ferrari, P. Spoletini, Beatrice Donati, D. Zowghi, S. Gnesi","doi":"10.1109/RE.2017.15","DOIUrl":"https://doi.org/10.1109/RE.2017.15","url":null,"abstract":"In requirements elicitation interviews, ambiguities identified by analysts can help to disclose the tacit knowledge of customers. Indeed, ambiguities might reveal implicit or hard to express information that needs to be elicited. The perception of ambiguity might depend on the subject who is acting as analyst, and different analysts might identify different ambiguities in the same interview. Based on this intuition, we propose to investigate the difference between ambiguities explicitly revealed by an analyst during a requirements elicitation interview, and ambiguities annotated by a reviewer who listens to the interview recording, with the objective of defining a method for interview review. We performed an exploratory study in which two subjects listened to a set of customer-analyst interviews. Only in 26% of the cases the ambiguities revealed by the analysts matched with the ambiguities found by the reviewers. In 46% of the cases, ambiguities were found by the reviewers, and were not detected by the analysts. Based on these preliminary findings, we are currently performing a controlled experiment with students of two universities, which will be followed by a real-world case study with companies. This paper discusses the current results, together with our research plan.","PeriodicalId":176958,"journal":{"name":"2017 IEEE 25th International Requirements Engineering Conference (RE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116506523","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: 14