The Department of Defense is adopting model-based systems engineering in which models will replace the extensive amounts of documentation generated in developing a new system. This research examines how this shift from textual description of requirements to a model-based description will effect the requirements engineering process. Specifically, we ask whether engineers will be able to extract the same understanding of the system requirements from the models as they can from the traditional textual requirements specifications. This paper describes the theory and related work on the understandability of models and the performance of cognitive tasks such as requirements engineering. Our research into model representation is part of a larger effort on a theory of model relativity postulating that models affect how we think about the system of interest. In this paper, we present our exploratory research studies, discuss our research protocol, describe the research plan, and present the current status of our study.
{"title":"The Ability of Engineers to Extract Requirements from Models","authors":"R. Giachetti, Karen Holness, Mollie McGuire","doi":"10.1109/RE.2018.00-19","DOIUrl":"https://doi.org/10.1109/RE.2018.00-19","url":null,"abstract":"The Department of Defense is adopting model-based systems engineering in which models will replace the extensive amounts of documentation generated in developing a new system. This research examines how this shift from textual description of requirements to a model-based description will effect the requirements engineering process. Specifically, we ask whether engineers will be able to extract the same understanding of the system requirements from the models as they can from the traditional textual requirements specifications. This paper describes the theory and related work on the understandability of models and the performance of cognitive tasks such as requirements engineering. Our research into model representation is part of a larger effort on a theory of model relativity postulating that models affect how we think about the system of interest. In this paper, we present our exploratory research studies, discuss our research protocol, describe the research plan, and present the current status of our study.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129563327","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}
Gilles Dellaert, James P Fink, J. Cromer, Kelly Kelbel, Ami Masuda, Kyle Harimoto, Karla Lindeman, Tommy Lee, Allison Borts, Candy Behunin, G. Wade, Jeff Ballinger, L. Hudson, P. Hawk, Per Welinder, Tiffany Rivera, J.R. Sommer, Scott Frankel, Salem Vuckovich, Benicio Del Toro, G.B. Slavin, Guatam Gupta, Jessica Hudson, J. Suárez, Mike Escamilla, P. Greene, Paul J. Broderick, Brian Dennington, Bryan Southard, Catherine Paletta, Colby Trudeau, D. Steinert, Donna M. Wies, E. Wiesner, Waylon Caldwell, Jaimie Muehlhausen
{"title":"Donors and Sponsors","authors":"Gilles Dellaert, James P Fink, J. Cromer, Kelly Kelbel, Ami Masuda, Kyle Harimoto, Karla Lindeman, Tommy Lee, Allison Borts, Candy Behunin, G. Wade, Jeff Ballinger, L. Hudson, P. Hawk, Per Welinder, Tiffany Rivera, J.R. Sommer, Scott Frankel, Salem Vuckovich, Benicio Del Toro, G.B. Slavin, Guatam Gupta, Jessica Hudson, J. Suárez, Mike Escamilla, P. Greene, Paul J. Broderick, Brian Dennington, Bryan Southard, Catherine Paletta, Colby Trudeau, D. Steinert, Donna M. Wies, E. Wiesner, Waylon Caldwell, Jaimie Muehlhausen","doi":"10.1109/re.2018.00009","DOIUrl":"https://doi.org/10.1109/re.2018.00009","url":null,"abstract":"","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128672145","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}
Many User Experience (UX) activities are carried out during requirements engineering phases, e.g. understanding and assessing the UX of existing systems, and eliciting functional and non-functional requirements that improve UX. These activities are typically performed by requirements engineers who are non-UX experts. It is necessary to provide a good UX in order to ensure long-term motivation of users, especially in business applications. UX has various characteristics of differing importance; it can be difficult for RE engineers to grasp all characteristics of UX and to judge which characteristics are important and which need to be improved. We propose a two-step approach to solve these difficulties. The first step is the definition of a UX quality model and corresponding metrics. We propose an approach to calculate the UX score of a business application using the value of these metrics. The second step is a process to identify insufficient characteristics within the calculated UX score. In this paper we present the aforementioned approach to collect and calculate the UX score of a product, show how to identify serious UX-related problems as part of requirements engineering activities, and present the results obtained from an initial validation of our quality model and related questionnaire. With our approach, we enable RE experts who are non-UX experts to find the necessary requirements to improve UX.
{"title":"Focusing Requirements Elicitation by Using a UX Measurement Method","authors":"Kyoko Ohashi, Asako Katayama, Naoki Hasegawa, H. Kurihara, Rieko Yamamoto, Jörg Dörr, Dominik Magin","doi":"10.1109/RE.2018.00-26","DOIUrl":"https://doi.org/10.1109/RE.2018.00-26","url":null,"abstract":"Many User Experience (UX) activities are carried out during requirements engineering phases, e.g. understanding and assessing the UX of existing systems, and eliciting functional and non-functional requirements that improve UX. These activities are typically performed by requirements engineers who are non-UX experts. It is necessary to provide a good UX in order to ensure long-term motivation of users, especially in business applications. UX has various characteristics of differing importance; it can be difficult for RE engineers to grasp all characteristics of UX and to judge which characteristics are important and which need to be improved. We propose a two-step approach to solve these difficulties. The first step is the definition of a UX quality model and corresponding metrics. We propose an approach to calculate the UX score of a business application using the value of these metrics. The second step is a process to identify insufficient characteristics within the calculated UX score. In this paper we present the aforementioned approach to collect and calculate the UX score of a product, show how to identify serious UX-related problems as part of requirements engineering activities, and present the results obtained from an initial validation of our quality model and related questionnaire. With our approach, we enable RE experts who are non-UX experts to find the necessary requirements to improve UX.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131408163","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 paper presents a SuSoftPro tool for requirement engineers to analyse the requirements' impacts on system sustainability. To perform the analysis of system sustainability, the tool provides quantitative questionnaires for rating high-level requirements within sustainability dimensions via a Fuzzy Rating Scale method. Stakeholders' responses are analysed by applying Technique for Order Preference by Similarity to Ideal Solution. The tool presents sustainability as a five-star rating label, a visualisation of the degree for sustainability dimensions, and a bar graph that illustrates the sustainability level.
{"title":"SuSoftPro: Sustainability Profiling for Software","authors":"A. Alharthi, M. Spichkova, M. Hamilton","doi":"10.1109/RE.2018.00072","DOIUrl":"https://doi.org/10.1109/RE.2018.00072","url":null,"abstract":"The paper presents a SuSoftPro tool for requirement engineers to analyse the requirements' impacts on system sustainability. To perform the analysis of system sustainability, the tool provides quantitative questionnaires for rating high-level requirements within sustainability dimensions via a Fuzzy Rating Scale method. Stakeholders' responses are analysed by applying Technique for Order Preference by Similarity to Ideal Solution. The tool presents sustainability as a five-star rating label, a visualisation of the degree for sustainability dimensions, and a bar graph that illustrates the sustainability level.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125376249","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}
Mafalda Santos, Catarina Gralha, M. Goulão, João Araújo, A. Moreira
Context: i* is one of the most influential languages in the Requirements Engineering research community. Perhaps due to its complexity and low adoption in industry, it became a natural candidate for studies aiming at improving its concrete syntax and the stakeholders' ability to correctly interpret i* models. Objectives: We evaluate the impact of semantic transparency on understanding and reviewing i* models, in the presence of a language key. Methods: We performed a quasi-experiment comparing the standard i* concrete syntax with an alternative that has an increased semantic transparency. We asked 57 novice participants to perform understanding and reviewing tasks on i* models, and measured their accuracy, speed and ease, using metrics of task success, time and effort, collected with eye-tracking and participants' feedback. Results: We found no evidence of improved accuracy or speed attributable to the alternative concrete syntax. Although participants' perceived ease was similar, they devoted significantly less visual effort to the model and the provided language key, when using the alternative concrete syntax. Conclusions: The context provided by the model and language key may mitigate the i* symbol recognition deficit reported in previous works. However, the alternative concrete syntax required a significantly lower visual effort.
{"title":"On the Impact of Semantic Transparency on Understanding and Reviewing Social Goal Models","authors":"Mafalda Santos, Catarina Gralha, M. Goulão, João Araújo, A. Moreira","doi":"10.1109/RE.2018.00031","DOIUrl":"https://doi.org/10.1109/RE.2018.00031","url":null,"abstract":"Context: i* is one of the most influential languages in the Requirements Engineering research community. Perhaps due to its complexity and low adoption in industry, it became a natural candidate for studies aiming at improving its concrete syntax and the stakeholders' ability to correctly interpret i* models. Objectives: We evaluate the impact of semantic transparency on understanding and reviewing i* models, in the presence of a language key. Methods: We performed a quasi-experiment comparing the standard i* concrete syntax with an alternative that has an increased semantic transparency. We asked 57 novice participants to perform understanding and reviewing tasks on i* models, and measured their accuracy, speed and ease, using metrics of task success, time and effort, collected with eye-tracking and participants' feedback. Results: We found no evidence of improved accuracy or speed attributable to the alternative concrete syntax. Although participants' perceived ease was similar, they devoted significantly less visual effort to the model and the provided language key, when using the alternative concrete syntax. Conclusions: The context provided by the model and language key may mitigate the i* symbol recognition deficit reported in previous works. However, the alternative concrete syntax required a significantly lower visual effort.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126615443","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 General Data Protection Regulation (GDPR) aims to protect personal data of EU residents and can impose severe sanctions for non-compliance. Organizations are currently implementing various measures to ensure their software systems fulfill GDPR obligations such as identifying a legal basis for data processing or enforcing data anonymization. However, as regulations are formulated vaguely, it is difficult for practitioners to extract and operationalize legal requirements from the GDPR. This paper aims to help organizations understand the data protection obligations imposed by the GDPR and identify measures to ensure compliance. To achieve this goal, we propose GuideMe, a 6-step systematic approach that supports elicitation of solution requirements that link GDPR data protection obligations with the privacy controls that fulfill these obligations and that should be implemented in an organization's software system. We illustrate and evaluate our approach using an example of a university information system. Our results demonstrate that the solution requirements elicited using our approach are aligned with the recommendations of privacy experts and are expressed correctly.
{"title":"The Grace Period Has Ended: An Approach to Operationalize GDPR Requirements","authors":"Vanessa Ayala-Rivera, L. Pasquale","doi":"10.1109/RE.2018.00023","DOIUrl":"https://doi.org/10.1109/RE.2018.00023","url":null,"abstract":"The General Data Protection Regulation (GDPR) aims to protect personal data of EU residents and can impose severe sanctions for non-compliance. Organizations are currently implementing various measures to ensure their software systems fulfill GDPR obligations such as identifying a legal basis for data processing or enforcing data anonymization. However, as regulations are formulated vaguely, it is difficult for practitioners to extract and operationalize legal requirements from the GDPR. This paper aims to help organizations understand the data protection obligations imposed by the GDPR and identify measures to ensure compliance. To achieve this goal, we propose GuideMe, a 6-step systematic approach that supports elicitation of solution requirements that link GDPR data protection obligations with the privacy controls that fulfill these obligations and that should be implemented in an organization's software system. We illustrate and evaluate our approach using an example of a university information system. Our results demonstrate that the solution requirements elicited using our approach are aligned with the recommendations of privacy experts and are expressed correctly.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114137913","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 requirements problem consists of transforming stakeholder requirements - however informal, ambiguous, conflicting, unattainable, imprecise and incomplete – into a consistent, complete and realizable specification through a systematic process. We propose a refinement calculus for requirements engineering (CaRE) for solving this problem, which takes into account the typically dialectic nature of requirements activities. The calculus casts the requirement problem as an iterative argument between stakeholders and requirements engineers, where posited requirements are attacked for being ambiguous, incomplete, etc. and refined into new requirements that address the defect pointed out by the attack. Refinements are carried out by operators provided by CaRE that refine (e.g., strengthen, weaken, decompose) existing requirements, to build a refinement graph. The semantics of the operators is provided by means of argumentation theory. Examples are given to illustrate the elements of our proposal.
{"title":"CaRE: A Refinement Calculus for Requirements Engineering Based on Argumentation Semantics","authors":"Yehia Elrakaiby, Alessio Ferrari, J. Mylopoulos","doi":"10.1109/RE.2018.00-24","DOIUrl":"https://doi.org/10.1109/RE.2018.00-24","url":null,"abstract":"The requirements problem consists of transforming stakeholder requirements - however informal, ambiguous, conflicting, unattainable, imprecise and incomplete – into a consistent, complete and realizable specification through a systematic process. We propose a refinement calculus for requirements engineering (CaRE) for solving this problem, which takes into account the typically dialectic nature of requirements activities. The calculus casts the requirement problem as an iterative argument between stakeholders and requirements engineers, where posited requirements are attacked for being ambiguous, incomplete, etc. and refined into new requirements that address the defect pointed out by the attack. Refinements are carried out by operators provided by CaRE that refine (e.g., strengthen, weaken, decompose) existing requirements, to build a refinement graph. The semantics of the operators is provided by means of argumentation theory. Examples are given to illustrate the elements of our proposal.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"20 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114103481","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}
S. Ågren, E. Knauss, Rogardt Heldal, Patrizio Pelliccione, Gösta Malmqvist, Jonas Bodén
Context: Historically, automotive manufacturers have adopted rigid requirements engineering processes, which allowed them to meet safety-critical requirements while integrating thousands of physical and software components into a highly complex and differentiated product. Nowadays, needs of improving development speed are pushing companies in this domain towards new ways of developing software. Objectives: We aim at obtaining a manager perspective on how the goal to increase development speed impacts how software intense automotive systems are developed and their requirements managed. Methods: We used a qualitative multiple-case study, based on 20 semi-structured interviews, at two automotive manufacturers. Our sampling strategy focuses on manager roles, complemented with technical specialists. Results: We found that both a requirements style dominated by safety concerns, and decomposition of requirements over many levels of abstraction impact development speed negatively. Furthermore, the use of requirements as part of legal contracts with suppliers hiders fast collaboration. Suggestions for potential improvements include domain-specific tooling, model-based requirements, test automation, and a combination of lightweight pre-development requirements engineering with precise specifications post-development. Conclusions: We offer an empirical account of expectations and needs for new requirements engineering approaches in the automotive domain, necessary to coordinate hundreds of collaborating organizations developing software-intensive and potentially safety-critical systems.
{"title":"The Manager Perspective on Requirements Impact on Automotive Systems Development Speed","authors":"S. Ågren, E. Knauss, Rogardt Heldal, Patrizio Pelliccione, Gösta Malmqvist, Jonas Bodén","doi":"10.1109/RE.2018.00-55","DOIUrl":"https://doi.org/10.1109/RE.2018.00-55","url":null,"abstract":"Context: Historically, automotive manufacturers have adopted rigid requirements engineering processes, which allowed them to meet safety-critical requirements while integrating thousands of physical and software components into a highly complex and differentiated product. Nowadays, needs of improving development speed are pushing companies in this domain towards new ways of developing software. Objectives: We aim at obtaining a manager perspective on how the goal to increase development speed impacts how software intense automotive systems are developed and their requirements managed. Methods: We used a qualitative multiple-case study, based on 20 semi-structured interviews, at two automotive manufacturers. Our sampling strategy focuses on manager roles, complemented with technical specialists. Results: We found that both a requirements style dominated by safety concerns, and decomposition of requirements over many levels of abstraction impact development speed negatively. Furthermore, the use of requirements as part of legal contracts with suppliers hiders fast collaboration. Suggestions for potential improvements include domain-specific tooling, model-based requirements, test automation, and a combination of lightweight pre-development requirements engineering with precise specifications post-development. Conclusions: We offer an empirical account of expectations and needs for new requirements engineering approaches in the automotive domain, necessary to coordinate hundreds of collaborating organizations developing software-intensive and potentially safety-critical systems.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114249789","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}
Requirements traceability provides critical support throughout all phases of software engineering. Automated tracing based on information retrieval (IR) reduces the effort required to perform a manual trace. Unfortunately, IR-based trace recovery suffers from low precision due to polysemy, which refers to the coexistence of multiple meanings for a term appearing in different requirements. Latent semantic indexing (LSI) has been introduced as a method to tackle polysemy, as well as synonymy. However, little is known about the scope and significance of polysemous terms in requirements tracing. While quantifying the effect, we present a novel method based on artificial neural networks (ANN) to enhance the capability of automatically resolving polysemous terms. The core idea is to build an ANN model which leverages a term's highest-scoring coreferences in different requirements to learn whether this term has the same meaning in those requirements. Experimental results based on 2 benchmark datasets and 6 long-lived open-source software projects show that our approach outperforms LSI on identifying polysemous terms and hence increasing the precision of automated tracing.
{"title":"Enhancing Automated Requirements Traceability by Resolving Polysemy","authors":"Wentao Wang, Nan Niu, Hui Liu, Zhendong Niu","doi":"10.1109/RE.2018.00-53","DOIUrl":"https://doi.org/10.1109/RE.2018.00-53","url":null,"abstract":"Requirements traceability provides critical support throughout all phases of software engineering. Automated tracing based on information retrieval (IR) reduces the effort required to perform a manual trace. Unfortunately, IR-based trace recovery suffers from low precision due to polysemy, which refers to the coexistence of multiple meanings for a term appearing in different requirements. Latent semantic indexing (LSI) has been introduced as a method to tackle polysemy, as well as synonymy. However, little is known about the scope and significance of polysemous terms in requirements tracing. While quantifying the effect, we present a novel method based on artificial neural networks (ANN) to enhance the capability of automatically resolving polysemous terms. The core idea is to build an ANN model which leverages a term's highest-scoring coreferences in different requirements to learn whether this term has the same meaning in those requirements. Experimental results based on 2 benchmark datasets and 6 long-lived open-source software projects show that our approach outperforms LSI on identifying polysemous terms and hence increasing the precision of automated tracing.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889636","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}
Salome Maro, Jan-Philipp Steghöfer, J. Hayes, J. Cleland-Huang, M. Staron
Automated traceability has been investigated for over a decade with promising results. However, a human analyst is needed to vet the generated trace links to ensure their quality. The process of vetting trace links is not trivial and while previous studies have analyzed the performance of the human analyst, they have not focused on the analyst's information needs. The aim of this study is to investigate what context information the human analyst needs. We used design science research, in which we conducted interviews with ten practitioners in the traceability area to understand the information needed by human analysts. We then compared the information collected from the interviews with existing literature. We created a prototype tool that presents this information to the human analyst. To further understand the role of context information, we conducted a controlled experiment with 33 participants. Our interviews reveal that human analysts need information from three different sources: 1) from the artifacts connected by the link, 2) from the traceability information model, and 3) from the tracing algorithm. The experiment results show that the content of the connected artifacts is more useful to the analyst than the contextual information of the artifacts.
{"title":"Vetting Automatically Generated Trace Links: What Information is Useful to Human Analysts?","authors":"Salome Maro, Jan-Philipp Steghöfer, J. Hayes, J. Cleland-Huang, M. Staron","doi":"10.1109/RE.2018.00-52","DOIUrl":"https://doi.org/10.1109/RE.2018.00-52","url":null,"abstract":"Automated traceability has been investigated for over a decade with promising results. However, a human analyst is needed to vet the generated trace links to ensure their quality. The process of vetting trace links is not trivial and while previous studies have analyzed the performance of the human analyst, they have not focused on the analyst's information needs. The aim of this study is to investigate what context information the human analyst needs. We used design science research, in which we conducted interviews with ten practitioners in the traceability area to understand the information needed by human analysts. We then compared the information collected from the interviews with existing literature. We created a prototype tool that presents this information to the human analyst. To further understand the role of context information, we conducted a controlled experiment with 33 participants. Our interviews reveal that human analysts need information from three different sources: 1) from the artifacts connected by the link, 2) from the traceability information model, and 3) from the tracing algorithm. The experiment results show that the content of the connected artifacts is more useful to the analyst than the contextual information of the artifacts.","PeriodicalId":445032,"journal":{"name":"2018 IEEE 26th International Requirements Engineering Conference (RE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115172501","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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