FormReq is a workshop aiming at bringing together practitioner and academics that aim at contributing and discussing towards requirements formalization
FormReq是一个研讨会，旨在将从业者和学者聚集在一起，旨在为需求形式化做出贡献和讨论
{"title":"FormReq@RE2019 Preface","authors":"S. Ebersold, Régine Laleau, M. Mazzara","doi":"10.1109/rew.2019.00023","DOIUrl":"https://doi.org/10.1109/rew.2019.00023","url":null,"abstract":"FormReq is a workshop aiming at bringing together practitioner and academics that aim at contributing and discussing towards requirements formalization","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134383263","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}
Mona Rahimi, Jin L. C. Guo, Sahar Kokaly, M. Chechik
In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. Instead, they "learn" existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, their ability to extract patterns and to behave accordingly is specifically useful for hard-to-specify concepts in certain safety critical domains (e.g., the definition of a pedestrian in a pedestrian detection component in a vehicle). However, the lack of requirements specifications on their behaviors makes further software engineering tasks challenging for such components. This is especially concerning for tasks such as safety assessment and assurance. In this position paper, we call for more attention from the requirements engineering community on supporting the specification of requirements for MLCs in safety critical domains. Towards that end, we propose an approach to improve the process of requirements specification in which an MLC is developed and operates by explicitly specifying domain-related concepts. Our approach extracts a universally accepted benchmark for hard-to-specify concepts (e.g., "pedestrian") and can be used to identify gaps in the associated dataset and the constructed machine-learned model.
{"title":"Toward Requirements Specification for Machine-Learned Components","authors":"Mona Rahimi, Jin L. C. Guo, Sahar Kokaly, M. Chechik","doi":"10.1109/REW.2019.00049","DOIUrl":"https://doi.org/10.1109/REW.2019.00049","url":null,"abstract":"In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. Instead, they \"learn\" existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, their ability to extract patterns and to behave accordingly is specifically useful for hard-to-specify concepts in certain safety critical domains (e.g., the definition of a pedestrian in a pedestrian detection component in a vehicle). However, the lack of requirements specifications on their behaviors makes further software engineering tasks challenging for such components. This is especially concerning for tasks such as safety assessment and assurance. In this position paper, we call for more attention from the requirements engineering community on supporting the specification of requirements for MLCs in safety critical domains. Towards that end, we propose an approach to improve the process of requirements specification in which an MLC is developed and operates by explicitly specifying domain-related concepts. Our approach extracts a universally accepted benchmark for hard-to-specify concepts (e.g., \"pedestrian\") and can be used to identify gaps in the associated dataset and the constructed machine-learned model.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129374023","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}
Gabriel Negash, Chun Ming Liang, Feras Al Taha, Nadin Bou Khzam, G. Mussbacher
The User Requirements Notation (URN) is a Requirements Engineering modeling language published by the International Telecommunication Union (ITU) to formally specify and analyze what a user would expect from a system. In particular, URN allows the modeling of use cases and scenarios of a system with Use Case Maps (UCM). A key benefit of formalizing these models is the added ability to better analyze them; thus gaining insight to improve quality and understanding of the requirements of the system and its capabilities. Existing traversal mechanisms which analyze UCM do not well reflect the inherent stochasticity of system or user interactions, because they are typically designed for visualization purposes rather than simulation and debugging. We propose a novel traversal mechanism that (i) better reflects real systems by incorporating non-determinism, (ii) considers multiple independent scenarios running concurrently, (iii) implements the UCM concept of map instances, and (iv) consequently enables automated simulation and execution as well as user-driven forward and backward debugging of UCM. We validate the novel traversal mechanism by applying it to a crisis response mobile app that allows a first responder to step forwards and backwards through crisis response actions.
用户需求符号(URN)是由国际电信联盟(ITU)发布的一种需求工程建模语言，用于正式指定和分析用户对系统的期望。特别是，URN允许用用例图(use Case Maps, UCM)对系统的用例和场景进行建模。形式化这些模型的一个关键好处是增加了更好地分析它们的能力;从而获得洞察力，以提高质量，并理解系统及其功能的需求。分析UCM的现有遍历机制不能很好地反映系统或用户交互的固有随机性，因为它们通常是为了可视化目的而设计的，而不是为了模拟和调试。我们提出了一种新的遍历机制，它(i)通过纳入非确定性来更好地反映真实系统，(ii)考虑并发运行的多个独立场景，(iii)实现地图实例的UCM概念，以及(iv)因此能够自动模拟和执行以及用户驱动的UCM向前和向后调试。我们通过将其应用于一个危机响应移动应用程序来验证这种新的遍历机制，该应用程序允许第一响应者在危机响应行动中向前和向后迈步。
{"title":"Non-Deterministic Use Case Map Traversal Algorithm for Scenario Simulation and Debugging","authors":"Gabriel Negash, Chun Ming Liang, Feras Al Taha, Nadin Bou Khzam, G. Mussbacher","doi":"10.1109/REW.2019.00014","DOIUrl":"https://doi.org/10.1109/REW.2019.00014","url":null,"abstract":"The User Requirements Notation (URN) is a Requirements Engineering modeling language published by the International Telecommunication Union (ITU) to formally specify and analyze what a user would expect from a system. In particular, URN allows the modeling of use cases and scenarios of a system with Use Case Maps (UCM). A key benefit of formalizing these models is the added ability to better analyze them; thus gaining insight to improve quality and understanding of the requirements of the system and its capabilities. Existing traversal mechanisms which analyze UCM do not well reflect the inherent stochasticity of system or user interactions, because they are typically designed for visualization purposes rather than simulation and debugging. We propose a novel traversal mechanism that (i) better reflects real systems by incorporating non-determinism, (ii) considers multiple independent scenarios running concurrently, (iii) implements the UCM concept of map instances, and (iv) consequently enables automated simulation and execution as well as user-driven forward and backward debugging of UCM. We validate the novel traversal mechanism by applying it to a crisis response mobile app that allows a first responder to step forwards and backwards through crisis response actions.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130293952","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}
This paper presents our initial experiences with employing option theory and NPV techniques for optimizing waste reduction in requirements scoping. Inspired by financial market theories, we analyze a large requirements scoping decision making history from the mobile handset domain. We outline how we can optimize waste reduction in requirements scoping by modeling the neutral, positive and negative scenarios, giving each of the scenarios appropriate budget and development team commitment.
{"title":"Using Financial Valuation Techniques to Minimize Waste in Requirements Scoping","authors":"Marcin Ocieszak, K. Wnuk, David Callele","doi":"10.1109/REW.2019.00007","DOIUrl":"https://doi.org/10.1109/REW.2019.00007","url":null,"abstract":"This paper presents our initial experiences with employing option theory and NPV techniques for optimizing waste reduction in requirements scoping. Inspired by financial market theories, we analyze a large requirements scoping decision making history from the mobile handset domain. We outline how we can optimize waste reduction in requirements scoping by modeling the neutral, positive and negative scenarios, giving each of the scenarios appropriate budget and development team commitment.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123004143","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}
This talk attempts to explain why formal methods are not being used to develop large-scale software-intensive computer-based systems by appealing to the Reference Model for Requirements and Specifications by Gunter, Gunter, Jackson, and Zave.
{"title":"The Requirements Engineering Reference Model: A Fundamental Impediment to Using Formal Methods in Software Systems Development","authors":"D. Berry","doi":"10.1109/REW.2019.00024","DOIUrl":"https://doi.org/10.1109/REW.2019.00024","url":null,"abstract":"This talk attempts to explain why formal methods are not being used to develop large-scale software-intensive computer-based systems by appealing to the Reference Model for Requirements and Specifications by Gunter, Gunter, Jackson, and Zave.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115517044","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}
Sentiment analysis tools are becoming increasingly more prevalent in the software engineering research community. In this data showcase paper, we present a set of twenty-two software requirements specification (SRS) documents that have been preprocessed and subsequently analyzed using the Senti4SD sentiment analysis tool. As part of our preliminary research, we compared the result of the sentiment analysis of the SRS documents and other non-related documents and found that the SRS documents were 6% more neutral than other non-related documents. Finally, we also present a number of research questions that we believe the research community might be able to answer using our published data.
{"title":"What Can the Sentiment of a Software Requirements Specification Document Tell Us?","authors":"Colin M. Werner, Ze Shi Li, Neil A. Ernst","doi":"10.1109/REW.2019.00022","DOIUrl":"https://doi.org/10.1109/REW.2019.00022","url":null,"abstract":"Sentiment analysis tools are becoming increasingly more prevalent in the software engineering research community. In this data showcase paper, we present a set of twenty-two software requirements specification (SRS) documents that have been preprocessed and subsequently analyzed using the Senti4SD sentiment analysis tool. As part of our preliminary research, we compared the result of the sentiment analysis of the SRS documents and other non-related documents and found that the SRS documents were 6% more neutral than other non-related documents. Finally, we also present a number of research questions that we believe the research community might be able to answer using our published data.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138342","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}
Data is the driver of artificial intelligence in requirements engineering. While some applications may lend themselves to training sets that are easily accessible (such as sentiment detection, feature request classification, requirements prioritization), other tasks face data challenges. Tracing and domain model building are examples of applications where data is not easily found or in the proper format or with the necessary metadata to support deep learning, machine learning, or other artificial intelligence techniques. This paper surveys datasets available from sources such as the Center of Excellence for Software and Systems Traceability and provides valuable metadata that can be used by re-searchers or practitioners when deciding what datasets to use, what aspects of datasets to use, what features to use in deep learning, and more.
{"title":"Toward Improved Artificial Intelligence in Requirements Engineering: Metadata for Tracing Datasets","authors":"J. Hayes, Jared Payne, Mallory Leppelmeier","doi":"10.1109/REW.2019.00052","DOIUrl":"https://doi.org/10.1109/REW.2019.00052","url":null,"abstract":"Data is the driver of artificial intelligence in requirements engineering. While some applications may lend themselves to training sets that are easily accessible (such as sentiment detection, feature request classification, requirements prioritization), other tasks face data challenges. Tracing and domain model building are examples of applications where data is not easily found or in the proper format or with the necessary metadata to support deep learning, machine learning, or other artificial intelligence techniques. This paper surveys datasets available from sources such as the Center of Excellence for Software and Systems Traceability and provides valuable metadata that can be used by re-searchers or practitioners when deciding what datasets to use, what aspects of datasets to use, what features to use in deep learning, and more.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116766636","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}
App store mining has proven to be a promising technique for requirements elicitation as companies can gain valuable knowledge to maintain and evolve existing apps. However, despite first advancements in using mining techniques for requirements elicitation, little is yet known how to distill requirements for new apps based on existing (similar) solutions and how exactly practitioners would benefit from such a technique. In the proposed work, we focus on exploring information (e.g. app store data) provided by the crowd about existing solutions to identify key features of applications in a particular domain. We argue that these discovered features and other related influential aspects (e.g. ratings) can help practitioners(e.g. software developer) to identify potential key features for new applications. To support this argument, we first conducted an interview study with practitioners to understand the extent to which such an approach would find champions in practice. In this paper, we present the first results of our ongoing research in the context of a larger road-map. Our interview study confirms that practitioners see the need for our envisioned approach. Furthermore, we present an early conceptual solution to discuss the feasibility of our approach. However, this manuscript is also intended to foster discussions on the extent to which machine learning can and should be applied to elicit automated requirements on crowd generated data on different forums and to identify further collaborations in this endeavor.
{"title":"Generating Requirements Out of Thin Air: Towards Automated Feature Identification for New Apps","authors":"Tahira Iqbal, N. Seyff, Daniel Méndez Fernández","doi":"10.1109/REW.2019.00040","DOIUrl":"https://doi.org/10.1109/REW.2019.00040","url":null,"abstract":"App store mining has proven to be a promising technique for requirements elicitation as companies can gain valuable knowledge to maintain and evolve existing apps. However, despite first advancements in using mining techniques for requirements elicitation, little is yet known how to distill requirements for new apps based on existing (similar) solutions and how exactly practitioners would benefit from such a technique. In the proposed work, we focus on exploring information (e.g. app store data) provided by the crowd about existing solutions to identify key features of applications in a particular domain. We argue that these discovered features and other related influential aspects (e.g. ratings) can help practitioners(e.g. software developer) to identify potential key features for new applications. To support this argument, we first conducted an interview study with practitioners to understand the extent to which such an approach would find champions in practice. In this paper, we present the first results of our ongoing research in the context of a larger road-map. Our interview study confirms that practitioners see the need for our envisioned approach. Furthermore, we present an early conceptual solution to discuss the feasibility of our approach. However, this manuscript is also intended to foster discussions on the extent to which machine learning can and should be applied to elicit automated requirements on crowd generated data on different forums and to identify further collaborations in this endeavor.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121843855","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}
Recently, a stringent set of privacy regulations, the General Data Protection Regulation (GDPR), was enacted in the European Union, which can be considered a privacy non-functional requirement (NFR). As a result, an organization that collects or processes data from European citizens must adhere to the GDPR. Previous studies have shown that compliance to the GDPR poses a number of challenges, which we have confirmed in our own research. In this paper, we describe our ongoing research collaboration with a startup organization that is adopting the GDPR. In addition, during the course of our research, we found that our industry collaborator, practices continuous integration (CI) like many other organizations. The number of organizations adopting CI has increased since Fowler first published his definition of CI. As such, another aspect of our current research is exploring the effects of CI on privacy NFRs and other NFRs. Finally, we describe a design science approach to iteratively learn about industry challenges in GDPR compliance, NFRs in the context of CI, as well as our ongoing work creating a tool to potentially mitigate observed GDPR compliance challenges.
{"title":"Continuous Requirements: An Example Using GDPR","authors":"Ze Shi Li, Colin M. Werner, Neil A. Ernst","doi":"10.1109/REW.2019.00031","DOIUrl":"https://doi.org/10.1109/REW.2019.00031","url":null,"abstract":"Recently, a stringent set of privacy regulations, the General Data Protection Regulation (GDPR), was enacted in the European Union, which can be considered a privacy non-functional requirement (NFR). As a result, an organization that collects or processes data from European citizens must adhere to the GDPR. Previous studies have shown that compliance to the GDPR poses a number of challenges, which we have confirmed in our own research. In this paper, we describe our ongoing research collaboration with a startup organization that is adopting the GDPR. In addition, during the course of our research, we found that our industry collaborator, practices continuous integration (CI) like many other organizations. The number of organizations adopting CI has increased since Fowler first published his definition of CI. As such, another aspect of our current research is exploring the effects of CI on privacy NFRs and other NFRs. Finally, we describe a design science approach to iteratively learn about industry challenges in GDPR compliance, NFRs in the context of CI, as well as our ongoing work creating a tool to potentially mitigate observed GDPR compliance challenges.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126777947","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}
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