In continuous integration (CI) environments, the program is rapidly and frequently modified and integrated. This feature introduces significant challenges to testing processes conducted in these environments. Based on existing technology, a test case that fails frequently is likely to fail in future tests. Therefore, the historical execution results of test cases are essential to guide the test case prioritization (TCP) in the CI environment. Reinforcement learning involves solving sequential decision-making problems and is suitable for TCP in the CI environment. At present, most of the TCP techniques based on reinforcement learning rely on the current cycle historical failure information of test cases. They rarely consider more historical cycle information, as well as other influencing factors. In this paper, we discussed the occurrence frequency of test cases for the first time. We also considered all historical information of each test case and proposed three new reward function, which employs the percentage of historical failure and the failure distribution of test cases, which can guide the reinforcement learning process. We evaluate our method on five industrial data sets. The experimental results show that our method can effectively prioritize test cases and improve the cost-effectiveness of the CI process.
{"title":"Occurrence Frequency and All Historical Failure Information Based Method for TCP in CI","authors":"Y. Shang, Qianyu Li, Yang Yang, Zheng Li","doi":"10.1145/3379177.3388903","DOIUrl":"https://doi.org/10.1145/3379177.3388903","url":null,"abstract":"In continuous integration (CI) environments, the program is rapidly and frequently modified and integrated. This feature introduces significant challenges to testing processes conducted in these environments. Based on existing technology, a test case that fails frequently is likely to fail in future tests. Therefore, the historical execution results of test cases are essential to guide the test case prioritization (TCP) in the CI environment. Reinforcement learning involves solving sequential decision-making problems and is suitable for TCP in the CI environment. At present, most of the TCP techniques based on reinforcement learning rely on the current cycle historical failure information of test cases. They rarely consider more historical cycle information, as well as other influencing factors. In this paper, we discussed the occurrence frequency of test cases for the first time. We also considered all historical information of each test case and proposed three new reward function, which employs the percentage of historical failure and the failure distribution of test cases, which can guide the reinforcement learning process. We evaluate our method on five industrial data sets. The experimental results show that our method can effectively prioritize test cases and improve the cost-effectiveness of the CI process.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124214842","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 the field of safety-critical systems, manual reviews are important to ensure high-quality software and to satisfy legal obligations. When applying model-based engineering approaches, no longer are only textual requirements specifications or software code under review, but also model-based specification artifacts like behavioral requirements models. As such behavioral specifications are typically documented on a type-level, errors concerning the interactions between multiple system instances can go unnoticed in manual reviews. This is particularly the case when multiple system instances of the same system type are interacting during runtime, which is typical for cyber-physical systems where networks of cyber-physical systems form dynamically to fulfill an overall purpose. In this paper, we report on a controlled experiment whose results indicate that instance-level review diagrams have – compared to type-level diagrams – important positive effects on reviewing processes for behavioral specifications of cyber-physical systems. Specifically, the experiment provides empirical evidence that instance-level review diagrams are significantly more expressive and effective than type-level diagrams.
{"title":"Do Instance-level Review Diagrams Support Validation Processes of Cyber-Physical System Specifications : Results from a Controlled Experiment","authors":"Marian Daun, Jennifer Brings, Thorsten Weyer","doi":"10.1145/3379177.3388893","DOIUrl":"https://doi.org/10.1145/3379177.3388893","url":null,"abstract":"In the field of safety-critical systems, manual reviews are important to ensure high-quality software and to satisfy legal obligations. When applying model-based engineering approaches, no longer are only textual requirements specifications or software code under review, but also model-based specification artifacts like behavioral requirements models. As such behavioral specifications are typically documented on a type-level, errors concerning the interactions between multiple system instances can go unnoticed in manual reviews. This is particularly the case when multiple system instances of the same system type are interacting during runtime, which is typical for cyber-physical systems where networks of cyber-physical systems form dynamically to fulfill an overall purpose. In this paper, we report on a controlled experiment whose results indicate that instance-level review diagrams have – compared to type-level diagrams – important positive effects on reviewing processes for behavioral specifications of cyber-physical systems. Specifically, the experiment provides empirical evidence that instance-level review diagrams are significantly more expressive and effective than type-level diagrams.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125935206","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 development teams dedicate considerable resources to training newcomers. Newcomers are new developers to a software project. The software onboarding process is more complicated than onboarding into other organizations. It is much more challenging and time-consuming. The role of a mentor in onboarding newcomers in software engineering is well understood. However, the disruptions to the work of an experienced developer can reduce the quality of their work and job satisfaction. We propose a conversational bot that can help onboard newcomers to a software project instead of an experienced programmer. The bot will act as a mentor for the newcomer, thus putting less stress on experienced programmers. The bot will also be able to scan outside sources, such as stack overflow, for solutions to issues a newcomer may face. The newcomer will be able to interact with the bot using natural language. We will use this bot to assess improvements to code quality in future studies.
{"title":"Onboarding Bot for Newcomers to Software Engineering","authors":"James Dominic, Charles Ritter, Paige Rodeghero","doi":"10.1145/3379177.3388901","DOIUrl":"https://doi.org/10.1145/3379177.3388901","url":null,"abstract":"Software development teams dedicate considerable resources to training newcomers. Newcomers are new developers to a software project. The software onboarding process is more complicated than onboarding into other organizations. It is much more challenging and time-consuming. The role of a mentor in onboarding newcomers in software engineering is well understood. However, the disruptions to the work of an experienced developer can reduce the quality of their work and job satisfaction. We propose a conversational bot that can help onboard newcomers to a software project instead of an experienced programmer. The bot will act as a mentor for the newcomer, thus putting less stress on experienced programmers. The bot will also be able to scan outside sources, such as stack overflow, for solutions to issues a newcomer may face. The newcomer will be able to interact with the bot using natural language. We will use this bot to assess improvements to code quality in future studies.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129458542","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}
Nils Prenner, Carolin Unger-Windeler, K. Schneider
Agile software development methods promise shorter time-to-market and higher product quality, but lack the ability of long-term planning or coping with large projects. However, software companies often also want the ability of long-term planning, promised by traditional or plan-based methods. To benefit from the strengths of both approaches, software companies often use a combination of agile and plan-based methods, known as hybrid development approaches. These approaches strongly depend on the individual context and are customized. Therefore, companies have to organize their hybrid development approach individually. However, practitioners often have difficulties with the organization of hybrid approaches. The organization considers how the phases, activities, roles, and artifacts are arranged and connected. Research lacks the necessary detailed insight into how hybrid development approaches are organized to support practitioners. To gain better understanding of the organization of hybrid approaches, we conducted a systematic literature review to gather descriptions of hybrid approaches. We analyzed the found papers thoroughly and could identify three general patterns of how hybrid approaches are organized. We found that all these patterns are still based on Royce’s waterfall model and use the standard software engineering activities. Our findings shall help to lead further research and help practitioners to better organize their individual development approach.
{"title":"How are Hybrid Development Approaches Organized? - A Systematic Literature Review","authors":"Nils Prenner, Carolin Unger-Windeler, K. Schneider","doi":"10.1145/3379177.3388907","DOIUrl":"https://doi.org/10.1145/3379177.3388907","url":null,"abstract":"Agile software development methods promise shorter time-to-market and higher product quality, but lack the ability of long-term planning or coping with large projects. However, software companies often also want the ability of long-term planning, promised by traditional or plan-based methods. To benefit from the strengths of both approaches, software companies often use a combination of agile and plan-based methods, known as hybrid development approaches. These approaches strongly depend on the individual context and are customized. Therefore, companies have to organize their hybrid development approach individually. However, practitioners often have difficulties with the organization of hybrid approaches. The organization considers how the phases, activities, roles, and artifacts are arranged and connected. Research lacks the necessary detailed insight into how hybrid development approaches are organized to support practitioners. To gain better understanding of the organization of hybrid approaches, we conducted a systematic literature review to gather descriptions of hybrid approaches. We analyzed the found papers thoroughly and could identify three general patterns of how hybrid approaches are organized. We found that all these patterns are still based on Royce’s waterfall model and use the standard software engineering activities. Our findings shall help to lead further research and help practitioners to better organize their individual development approach.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121638208","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}
A. Munappy, D. I. Mattos, J. Bosch, H. H. Olsson, Anas Dakkak
The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage.
{"title":"From Ad-Hoc Data Analytics to DataOps","authors":"A. Munappy, D. I. Mattos, J. Bosch, H. H. Olsson, Anas Dakkak","doi":"10.1145/3379177.3388909","DOIUrl":"https://doi.org/10.1145/3379177.3388909","url":null,"abstract":"The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133464545","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}
Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.
{"title":"Developing ML/DL Models: A Design Framework","authors":"Meenu Mary John, H. H. Olsson, J. Bosch","doi":"10.1145/3379177.3388892","DOIUrl":"https://doi.org/10.1145/3379177.3388892","url":null,"abstract":"Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121461415","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}
Thomas T. Hildebrandt, Amine Abbad Andaloussi, L. Christensen, S. Debois, Nicklas Pape Healy, Hugo A. López, Morten Marquard, N. H. Møller, Anette C. M. Petersen, Tijs Slaats, B. Weber
We report on a new approach to co-creating adaptive case management systems jointly with end-users, developed in the context of the Effective co-created and compliant adaptive case Management Systems for Knowledge Workers (EcoKnow.org) research project. The approach is based on knowledge from prior ethnographic field studies and research in the declarative Dynamic Condition Response (DCR) technology for model-driven design of case management systems. The approach was tested in an operational environment jointly with the danish municipality of Syddjurs by conducting a service-design project and implementing an open source case manager tool and a new highlighter tool for mapping between textual specifications and the DCR notation. The design method and technologies were evaluated by understandability studies with end-users. The study showed that the development could be done in just 6 months, and that the new highlighter tool in combination with the traditional design and simulation tools, supports domain experts formalise and provide traceability between their interpretations of textual specifications and the formal models.
{"title":"EcoKnow: Engineering Effective, Co-created and Compliant Adaptive Case Management Systems for Knowledge Workers","authors":"Thomas T. Hildebrandt, Amine Abbad Andaloussi, L. Christensen, S. Debois, Nicklas Pape Healy, Hugo A. López, Morten Marquard, N. H. Møller, Anette C. M. Petersen, Tijs Slaats, B. Weber","doi":"10.1145/3379177.3388908","DOIUrl":"https://doi.org/10.1145/3379177.3388908","url":null,"abstract":"We report on a new approach to co-creating adaptive case management systems jointly with end-users, developed in the context of the Effective co-created and compliant adaptive case Management Systems for Knowledge Workers (EcoKnow.org) research project. The approach is based on knowledge from prior ethnographic field studies and research in the declarative Dynamic Condition Response (DCR) technology for model-driven design of case management systems. The approach was tested in an operational environment jointly with the danish municipality of Syddjurs by conducting a service-design project and implementing an open source case manager tool and a new highlighter tool for mapping between textual specifications and the DCR notation. The design method and technologies were evaluated by understandability studies with end-users. The study showed that the development could be done in just 6 months, and that the new highlighter tool in combination with the traditional design and simulation tools, supports domain experts formalise and provide traceability between their interpretations of textual specifications and the formal models.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114083296","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}
Christoph Mayr-Dorn, Johann Tuder, Alexander Egyed
Organizational factors such as team structure, coordination among engineers, or processes have a significant impact on software quality and development progress. Projects often take much longer to complete than planned and miscommunications among engineers are common. Yet, the process for exploring the project-specific or organization-specific root causes why this happens is still poorly supported. Investigations are cumbersome and require significant effort. In the context of this industrial case study, our industry partner was interested in measuring and assessing how the organization structure and issue handling processes ultimately affected software quality and time. Reducing the effort of such investigations/retrospectives and speeding up fact finding is important as it allows for more frequent, informed engineering process improvements and feedback to managers, team leads, and engineers. This paper describes our approach of pairing process metrics with visual historical inspection of issues. Stakeholders such as managers, team leads, or quality assurance engineers inspect metrics (and deviations from expected values) for individual issues and utilize a historical visualization of the affected (and related) issues to obtain insights into the reason for the metric (deviation) and its root cause. We demonstrate the usefulness of our approach based on our ProcessInspector prototype providing access to data on four real industry projects and a qualitative evaluation with team leads and group leads from our industry partner.
{"title":"Process Inspection Support: an Industrial Case Study","authors":"Christoph Mayr-Dorn, Johann Tuder, Alexander Egyed","doi":"10.1145/3379177.3388900","DOIUrl":"https://doi.org/10.1145/3379177.3388900","url":null,"abstract":"Organizational factors such as team structure, coordination among engineers, or processes have a significant impact on software quality and development progress. Projects often take much longer to complete than planned and miscommunications among engineers are common. Yet, the process for exploring the project-specific or organization-specific root causes why this happens is still poorly supported. Investigations are cumbersome and require significant effort. In the context of this industrial case study, our industry partner was interested in measuring and assessing how the organization structure and issue handling processes ultimately affected software quality and time. Reducing the effort of such investigations/retrospectives and speeding up fact finding is important as it allows for more frequent, informed engineering process improvements and feedback to managers, team leads, and engineers. This paper describes our approach of pairing process metrics with visual historical inspection of issues. Stakeholders such as managers, team leads, or quality assurance engineers inspect metrics (and deviations from expected values) for individual issues and utilize a historical visualization of the affected (and related) issues to obtain insights into the reason for the metric (deviation) and its root cause. We demonstrate the usefulness of our approach based on our ProcessInspector prototype providing access to data on four real industry projects and a qualitative evaluation with team leads and group leads from our industry partner.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124738434","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}
Rashidah Kasauli, Rebekka Wohlrab, E. Knauss, Jan-Philipp Steghöfer, Jennifer Horkoff, Salome Maro
Large-scale system development companies are increasingly adopting agile methods. While this adoption may improve lead-times, such companies need to balance two trade-offs: (i) the need to have a uniform, consistent development method on system level with the need for specialised methods for teams in different disciplines (e.g., hardware, software, mechanics, sales, support); (ii) the need for comprehensive documentation on system level with the need to have lightweight documentation enabling iterative and agile work. With specialised methods for teams, isolated teams work within larger ecosystems of plan-driven culture, i.e., teams become agile “islands”. At the boundaries, these teams share knowledge which needs to be managed well for a correct system to be developed. While it is useful to support diverse and specialised methods, it is important to understand which islands are repeatedly encountered, the reasons or factors triggering their existence, and how best to handle coordination between them. Based on a multiple case study, this work presents a catalogue of islands and the boundary objects between them. We believe this work will be beneficial to practitioners aiming to understand their ecosystems and researchers addressing communication and coordination challenges in large-scale development.
{"title":"Charting Coordination Needs in Large-Scale Agile Organisations with Boundary Objects and Methodological Islands","authors":"Rashidah Kasauli, Rebekka Wohlrab, E. Knauss, Jan-Philipp Steghöfer, Jennifer Horkoff, Salome Maro","doi":"10.1145/3379177.3388897","DOIUrl":"https://doi.org/10.1145/3379177.3388897","url":null,"abstract":"Large-scale system development companies are increasingly adopting agile methods. While this adoption may improve lead-times, such companies need to balance two trade-offs: (i) the need to have a uniform, consistent development method on system level with the need for specialised methods for teams in different disciplines (e.g., hardware, software, mechanics, sales, support); (ii) the need for comprehensive documentation on system level with the need to have lightweight documentation enabling iterative and agile work. With specialised methods for teams, isolated teams work within larger ecosystems of plan-driven culture, i.e., teams become agile “islands”. At the boundaries, these teams share knowledge which needs to be managed well for a correct system to be developed. While it is useful to support diverse and specialised methods, it is important to understand which islands are repeatedly encountered, the reasons or factors triggering their existence, and how best to handle coordination between them. Based on a multiple case study, this work presents a catalogue of islands and the boundary objects between them. We believe this work will be beneficial to practitioners aiming to understand their ecosystems and researchers addressing communication and coordination challenges in large-scale development.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121725241","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}
Development and Operations (DevOps), a particular type of Continuous Software Engineering, has become a popular Software System Engineering paradigm. Software architecture is critical in succeeding with DevOps. However, there is little evidence-based knowledge of how software systems are architected in the industry to enable and support DevOps. Since architectural decisions, along with their rationales and implications, are very important in the architecting process, we performed an industrial case study that has empirically identified and synthesized the key architectural decisions considered essential to DevOps transformation by two software development teams. Our study also reveals that apart from the chosen architecture style, DevOps works best with modular architectures. In addition, we found that the performance of the studied teams can improve in DevOps if operations specialists are added to the teams to perform the operations tasks that require advanced expertise. Finally, investment in testing is inevitable for the teams if they want to release software changes faster.
{"title":"On the Role of Software Architecture in DevOps Transformation: An Industrial Case Study","authors":"Mojtaba Shahin, M. Babar","doi":"10.1145/3379177.3388891","DOIUrl":"https://doi.org/10.1145/3379177.3388891","url":null,"abstract":"Development and Operations (DevOps), a particular type of Continuous Software Engineering, has become a popular Software System Engineering paradigm. Software architecture is critical in succeeding with DevOps. However, there is little evidence-based knowledge of how software systems are architected in the industry to enable and support DevOps. Since architectural decisions, along with their rationales and implications, are very important in the architecting process, we performed an industrial case study that has empirically identified and synthesized the key architectural decisions considered essential to DevOps transformation by two software development teams. Our study also reveals that apart from the chosen architecture style, DevOps works best with modular architectures. In addition, we found that the performance of the studied teams can improve in DevOps if operations specialists are added to the teams to perform the operations tasks that require advanced expertise. Finally, investment in testing is inevitable for the teams if they want to release software changes faster.","PeriodicalId":299473,"journal":{"name":"2020 IEEE/ACM International Conference on Software and System Processes (ICSSP)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125386425","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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