N. Bencomo, G. Mussbacher, A. Moreira, J. Araújo, Pablo Sánchez
Preface to 9th International Workshop on Model-Driven Requirements Engineering
第九届模型驱动需求工程国际研讨会前言
{"title":"Preface to 9th International Workshop on Model-Driven Requirements Engineering","authors":"N. Bencomo, G. Mussbacher, A. Moreira, J. Araújo, Pablo Sánchez","doi":"10.1109/rew.2019.00009","DOIUrl":"https://doi.org/10.1109/rew.2019.00009","url":null,"abstract":"Preface to 9th International Workshop on Model-Driven Requirements Engineering","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"77 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":"122441659","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}
Jaspreet Bhatia, P. Murukannaiah, Nan Niu, F. Dalpiaz
Welcome to the 6th edition of the International Workshop on Artificial Intelligence for Requirements Engineering (AIRE'19). This interdisciplinary workshop is intended to explore and extend the synergies between Artificial Intelligence and Requirements Engineering. The AIRE workshop's aim is to: (i) study Requirements Engineering (RE) areas that may benefit from the application of AI techniques; and (ii) investigate how RE can be conducted for AI-based systems. The workshop is an established venue for inspiring a broad community to engage in interdisciplinary discussions concerning novel research directions for Requirements Engineering and Artificial Intelligence.
{"title":"The 6th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE'19)","authors":"Jaspreet Bhatia, P. Murukannaiah, Nan Niu, F. Dalpiaz","doi":"10.1109/rew.2019.00043","DOIUrl":"https://doi.org/10.1109/rew.2019.00043","url":null,"abstract":"Welcome to the 6th edition of the International Workshop on Artificial Intelligence for Requirements Engineering (AIRE'19). This interdisciplinary workshop is intended to explore and extend the synergies between Artificial Intelligence and Requirements Engineering. The AIRE workshop's aim is to: (i) study Requirements Engineering (RE) areas that may benefit from the application of AI techniques; and (ii) investigate how RE can be conducted for AI-based systems. The workshop is an established venue for inspiring a broad community to engage in interdisciplinary discussions concerning novel research directions for Requirements Engineering and Artificial Intelligence.","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":"131291836","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}
The Systems Modeling Language (SysML) represents a significant and increasing segment of industrial support for building critical systems. Because modeling is a human-centric activity, mistakes are unavoidable. Although there exist several software defect classifications, little is known about the mistakes pertaining to SysML modeling and the implications of those mistakes in model-driven requirements engineering. In this paper, we report a systematic mapping through which 42 SysML modeling mistakes are identified from 19 primary studies. With an emphasis on the evidence of industrial relevance, we further uncover that, despite some mistakes hurt requirements satisfaction, others help make the requirements more complete and the specifications more precise. Our work sheds light on understanding the scope of the SysML mistakes and checking requirements fulfillment in the face of the mistakes.
{"title":"SysML Modeling Mistakes and Their Impacts on Requirements","authors":"Mounifah Alenazi, Nan Niu, J. Savolainen","doi":"10.1109/REW.2019.00010","DOIUrl":"https://doi.org/10.1109/REW.2019.00010","url":null,"abstract":"The Systems Modeling Language (SysML) represents a significant and increasing segment of industrial support for building critical systems. Because modeling is a human-centric activity, mistakes are unavoidable. Although there exist several software defect classifications, little is known about the mistakes pertaining to SysML modeling and the implications of those mistakes in model-driven requirements engineering. In this paper, we report a systematic mapping through which 42 SysML modeling mistakes are identified from 19 primary studies. With an emphasis on the evidence of industrial relevance, we further uncover that, despite some mistakes hurt requirements satisfaction, others help make the requirements more complete and the specifications more precise. Our work sheds light on understanding the scope of the SysML mistakes and checking requirements fulfillment in the face of the mistakes.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"53 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":"134486105","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}
Software requirements are usually written in common natural language. An important quality criterion for each documented requirement is unambiguity. This simply means that all readers of the requirement must arrive at the same understanding of the requirement. Due to differences in the domain expertise of requirements engineer and other stakeholders of the project, it is possible that requirements contain several words that allow alternative interpretations. Our objective is to identify and detect domain specific ambiguous words in natural language text. This paper applies an NLP technique based on word embeddings to detect such ambiguous words. More specifically, we measure the ambiguity potential of most frequently used computer science (CS) words when they are used in other application areas or subdomains of engineering, e.g., aerospace, civil, petroleum, biomedical and environmental etc. Our extensive and detailed experiments with several different subdomains show that word embedding based techniques are very effective in identifying domain specific ambiguities. Our findings also demonstrate that this technique can be applied to documents of varying sizes. Finally, we provide pointers for future research.
{"title":"On the Use of Word Embeddings for Identifying Domain Specific Ambiguities in Requirements","authors":"S. Mishra, Arpit Sharma","doi":"10.1109/REW.2019.00048","DOIUrl":"https://doi.org/10.1109/REW.2019.00048","url":null,"abstract":"Software requirements are usually written in common natural language. An important quality criterion for each documented requirement is unambiguity. This simply means that all readers of the requirement must arrive at the same understanding of the requirement. Due to differences in the domain expertise of requirements engineer and other stakeholders of the project, it is possible that requirements contain several words that allow alternative interpretations. Our objective is to identify and detect domain specific ambiguous words in natural language text. This paper applies an NLP technique based on word embeddings to detect such ambiguous words. More specifically, we measure the ambiguity potential of most frequently used computer science (CS) words when they are used in other application areas or subdomains of engineering, e.g., aerospace, civil, petroleum, biomedical and environmental etc. Our extensive and detailed experiments with several different subdomains show that word embedding based techniques are very effective in identifying domain specific ambiguities. Our findings also demonstrate that this technique can be applied to documents of varying sizes. Finally, we provide pointers for future research.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"27 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":"117255675","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}
In 2018, the General Data Protection Regulation (GDPR) came into force, imposing strict laws aimed to protect the privacy of natural persons in member states of the European Union. However, the implications of the GDPR with respect to gathering, storing, and analyzing online user feedback — which is an important source of information for Crowd-based Requirements Engineering (CrowdRE) — have not been assessed yet. User feedback has been found to contain personal data, so the GDPR applies. It may be used for CrowdRE if conditions regarding data storage and handling are met and if, when used commercially, the duty to inform is carried out and the data subjects' rights and freedoms are respected. This can be a burden on the application of CrowdRE and might even inhibit its adoption. We propose a heuristic-based solution to anonymize the most prevalent types of personal data while crawling user feedback so that the data processing is no longer subject to GDPR.
{"title":"CrowdRE, User Feedback and GDPR: Towards Tackling GDPR Implications with Adequate Technical and Organizational Measures in an Effort-Minimal Way","authors":"Eduard C. Groen, M. Ochs","doi":"10.1109/REW.2019.00038","DOIUrl":"https://doi.org/10.1109/REW.2019.00038","url":null,"abstract":"In 2018, the General Data Protection Regulation (GDPR) came into force, imposing strict laws aimed to protect the privacy of natural persons in member states of the European Union. However, the implications of the GDPR with respect to gathering, storing, and analyzing online user feedback — which is an important source of information for Crowd-based Requirements Engineering (CrowdRE) — have not been assessed yet. User feedback has been found to contain personal data, so the GDPR applies. It may be used for CrowdRE if conditions regarding data storage and handling are met and if, when used commercially, the duty to inform is carried out and the data subjects' rights and freedoms are respected. This can be a burden on the application of CrowdRE and might even inhibit its adoption. We propose a heuristic-based solution to anonymize the most prevalent types of personal data while crawling user feedback so that the data processing is no longer subject to GDPR.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"20 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":"130593369","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}
Advanced Persistent Threat (APT) is one of the cyber threats that continuously attack specific targets exfiltrate information or destroy the system [1]. Because the attackers use various tools and methods according to the target, it is difficult to describe APT attack in a single pattern. Therefore, APT attacks are difficult to defend against with general countermeasures. In these days, systems consist of various components and related stakeholders, which makes it difficult to consider all the security concerns. In this paper, we propose an ontology knowledge base and its design process to recommend security requirements based on APT attack cases and system domain knowledge. The proposed knowledge base is divided into three parts; APT ontology, general security knowledge ontology, and domain-specific knowledge ontology. Each ontology can help to understand the security concerns in their knowledge. While integrating three ontologies into the problem domain ontology, the appropriate security requirements can be derived with the security requirements recommendation process. The proposed knowledge base and process can help to derive the security requirements while considering both real attacks and systems.
{"title":"Ontology-Driven Security Requirements Recommendation for APT Attack","authors":"Min-Ju Kim, Sangeeta Dey, Seok-Won Lee","doi":"10.1109/REW.2019.00032","DOIUrl":"https://doi.org/10.1109/REW.2019.00032","url":null,"abstract":"Advanced Persistent Threat (APT) is one of the cyber threats that continuously attack specific targets exfiltrate information or destroy the system [1]. Because the attackers use various tools and methods according to the target, it is difficult to describe APT attack in a single pattern. Therefore, APT attacks are difficult to defend against with general countermeasures. In these days, systems consist of various components and related stakeholders, which makes it difficult to consider all the security concerns. In this paper, we propose an ontology knowledge base and its design process to recommend security requirements based on APT attack cases and system domain knowledge. The proposed knowledge base is divided into three parts; APT ontology, general security knowledge ontology, and domain-specific knowledge ontology. Each ontology can help to understand the security concerns in their knowledge. While integrating three ontologies into the problem domain ontology, the appropriate security requirements can be derived with the security requirements recommendation process. The proposed knowledge base and process can help to derive the security requirements while considering both real attacks and systems.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"103 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":"124183902","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}
Preethu Rose Anish, A. Sainani, Abdul Ahmed, S. Ghaisas
In large multi-site multi-vendor projects, studying requirement documents to understand the problem domain and inferring possible solution to the posed problem is an important activity in Requirements Engineering. The process of reading User require-ments Specification (URS) to create Software Requirement Speci-fication (SRS) is a knowledge intensive activity that precedes sev-eral other important Software Engineering (SE) activities such as design and test plans. Automated Interpretation of the URS in terms of implementation-specific knowledge elements for software engineers' consumption has been reported in the past. The aim of such an interpretation is to reduce the effort associated with a manual extraction of knowledge elements and subsequently, their "translation" into primitives understood by those who must build the intended software. In this paper, we present a deep learning model for an implementation-centric classification of one such knowledge element, namely, business rules. We discuss an approach based on a Bidirectional Long Short Term Memory Network (BiLSTM) to capture the context information for each word, followed by an attention model to aggregate useful infor-mation from these words to get to the final classification. Our model adopts an end-to-end architecture that does not rely on any handcrafted features.
{"title":"Implementation-Centric Classification of Business Rules from Documents","authors":"Preethu Rose Anish, A. Sainani, Abdul Ahmed, S. Ghaisas","doi":"10.1109/REW.2019.00047","DOIUrl":"https://doi.org/10.1109/REW.2019.00047","url":null,"abstract":"In large multi-site multi-vendor projects, studying requirement documents to understand the problem domain and inferring possible solution to the posed problem is an important activity in Requirements Engineering. The process of reading User require-ments Specification (URS) to create Software Requirement Speci-fication (SRS) is a knowledge intensive activity that precedes sev-eral other important Software Engineering (SE) activities such as design and test plans. Automated Interpretation of the URS in terms of implementation-specific knowledge elements for software engineers' consumption has been reported in the past. The aim of such an interpretation is to reduce the effort associated with a manual extraction of knowledge elements and subsequently, their \"translation\" into primitives understood by those who must build the intended software. In this paper, we present a deep learning model for an implementation-centric classification of one such knowledge element, namely, business rules. We discuss an approach based on a Bidirectional Long Short Term Memory Network (BiLSTM) to capture the context information for each word, followed by an attention model to aggregate useful infor-mation from these words to get to the final classification. Our model adopts an end-to-end architecture that does not rely on any handcrafted features.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"3 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":"128179039","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}
Guaranteeing the correctness of critical and complex software and systems is a challenge that needs to be tackled right from the requirements engineering phase. This paper introduces two development patterns linked to the shape of requirements. The first one allows to automatically formalize a constraint and introduce it in an existing system. The second one is interested on requirements describing a sequence of operations. The verification activity is partly automated and the validation becomes easier to manage. The approach using these development patterns allow us an incremental development of formal specifications and their associated requirements, linked by a glossary. The case study of a hemodialysis system is used as a running example throughout this paper.
{"title":"Bridging the Gap Between Requirements Document and Formal Specifications using Development Patterns","authors":"Imen Sayar, J. Souquières","doi":"10.1109/REW.2019.00026","DOIUrl":"https://doi.org/10.1109/REW.2019.00026","url":null,"abstract":"Guaranteeing the correctness of critical and complex software and systems is a challenge that needs to be tackled right from the requirements engineering phase. This paper introduces two development patterns linked to the shape of requirements. The first one allows to automatically formalize a constraint and introduce it in an existing system. The second one is interested on requirements describing a sequence of operations. The verification activity is partly automated and the validation becomes easier to manage. The approach using these development patterns allow us an incremental development of formal specifications and their associated requirements, linked by a glossary. The case study of a hemodialysis system is used as a running example throughout this paper.","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":"129182741","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 identifies and tackles the challenges of the requirements engineering discipline when applied to development of AI-based complex systems. Due to their complex behaviour, there is an immanent need for a tailored development process for such systems. However, there is still no widely used and specifically tailored process in place to effectively and efficiently deal with requirements suitable for specifying a software solution that uses machine learning. By analysing the related work from software engineering and artificial intelligence fields, potential contributions have been recognized from agent-based software engineering and goal-oriented requirements engineering research, as well as examples from large product development companies. The challenges have been discussed, with proposals given how and when to tackle them. RE4AI taxonomy has also been outlined, to inform the tailoring of development process.
{"title":"Requirements Engineering Challenges in Building AI-Based Complex Systems","authors":"H. Belani, M. Vuković, Z. Car","doi":"10.1109/REW.2019.00051","DOIUrl":"https://doi.org/10.1109/REW.2019.00051","url":null,"abstract":"This paper identifies and tackles the challenges of the requirements engineering discipline when applied to development of AI-based complex systems. Due to their complex behaviour, there is an immanent need for a tailored development process for such systems. However, there is still no widely used and specifically tailored process in place to effectively and efficiently deal with requirements suitable for specifying a software solution that uses machine learning. By analysing the related work from software engineering and artificial intelligence fields, potential contributions have been recognized from agent-based software engineering and goal-oriented requirements engineering research, as well as examples from large product development companies. The challenges have been discussed, with proposals given how and when to tackle them. RE4AI taxonomy has also been outlined, to inform the tailoring of development process.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127604537","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}
Jannik Fischbach, Maximilian Junker, Andreas Vogelsang, Dietmar Freudenstein
[Context:] Model-based testing is an instrument for automated generation of test cases. It requires identifying requirements in documents, understanding them syntactically and semantically, and then translating them into a test model. One light-weight language for these test models are Cause-Effect-Graphs (CEG) that can be used to derive test cases. [Problem:] The creation of test models is laborious and we lack an automated solution that covers the entire process from requirement detection to test model creation. In addition, the majority of requirements is expressed in natural language (NL), which is hard to translate to test models automatically. [Principal Idea:] We build on the fact that not all NL requirements are equally unstructured. We found that 14% of the lines in requirements documents of our industry partner contain "pseudo-code"-like descriptions of business rules. We apply Machine Learning to identify such semi-structured requirements descriptions and propose a rule-based approach for their translation into CEGs. [Contribution:] We make three contributions: (1) an algorithm for the automatic detection of semi-structured requirements descriptions in documents, (2) an algorithm for the automatic translation of the identified requirements into a CEG and (3) a study demonstrating that our proposed solution leads to 86% time savings for test model creation without loss of quality.
{"title":"Automated Generation of Test Models from Semi-Structured Requirements","authors":"Jannik Fischbach, Maximilian Junker, Andreas Vogelsang, Dietmar Freudenstein","doi":"10.1109/REW.2019.00053","DOIUrl":"https://doi.org/10.1109/REW.2019.00053","url":null,"abstract":"[Context:] Model-based testing is an instrument for automated generation of test cases. It requires identifying requirements in documents, understanding them syntactically and semantically, and then translating them into a test model. One light-weight language for these test models are Cause-Effect-Graphs (CEG) that can be used to derive test cases. [Problem:] The creation of test models is laborious and we lack an automated solution that covers the entire process from requirement detection to test model creation. In addition, the majority of requirements is expressed in natural language (NL), which is hard to translate to test models automatically. [Principal Idea:] We build on the fact that not all NL requirements are equally unstructured. We found that 14% of the lines in requirements documents of our industry partner contain \"pseudo-code\"-like descriptions of business rules. We apply Machine Learning to identify such semi-structured requirements descriptions and propose a rule-based approach for their translation into CEGs. [Contribution:] We make three contributions: (1) an algorithm for the automatic detection of semi-structured requirements descriptions in documents, (2) an algorithm for the automatic translation of the identified requirements into a CEG and (3) a study demonstrating that our proposed solution leads to 86% time savings for test model creation without loss of quality.","PeriodicalId":166923,"journal":{"name":"2019 IEEE 27th International Requirements Engineering Conference Workshops (REW)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115565826","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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