Motivated and competent team members are a vital part of Agile Software development and make or break any project's success. Motivation is fostered by continuous progress and recognition of efforts. These concepts are founding pillars of the Scrum methodology, which focuses on self-organizing teams. The types of contributions Scrum development team members make to a project's progress are not only technical. However, a comprehensive model comprising the varied contributions in modern software engineering teams is not yet established. We propose a model that incorporates contributions of all Scrum roles, explicitly including those which are not directly related to project artifacts. It improves the visibility of performed tasks, acts as a starting point for team retrospection, and serves as a foundation for discussion in the research community.
{"title":"More than Code: Contributions in Scrum Software Engineering Teams","authors":"Frederike Ramin, Christoph Matthies, Ralf Teusner","doi":"10.1145/3387940.3392241","DOIUrl":"https://doi.org/10.1145/3387940.3392241","url":null,"abstract":"Motivated and competent team members are a vital part of Agile Software development and make or break any project's success. Motivation is fostered by continuous progress and recognition of efforts. These concepts are founding pillars of the Scrum methodology, which focuses on self-organizing teams. The types of contributions Scrum development team members make to a project's progress are not only technical. However, a comprehensive model comprising the varied contributions in modern software engineering teams is not yet established. We propose a model that incorporates contributions of all Scrum roles, explicitly including those which are not directly related to project artifacts. It improves the visibility of performed tasks, acts as a starting point for team retrospection, and serves as a foundation for discussion in the research community.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122196951","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}
Communication between a software development team and business partners is often a challenging task due to the different context of terms used in the information exchange. The various contexts in which the concepts are defined or used create slightly different semantic fields that can evolve into information and communication silos. Due to the silo effect, the necessary information is often inadequately forwarded to developers resulting in poorly specified software requirements or misinterpreted user feedback. Communication difficulties can be reduced by introducing a mapping between the semantic fields of the parties involved in the communication based on the commonly used terminologies. Our research aims to obtain a suitable semantic database in the form of a semantic network built from the Stack Overflow corpus, which can be considered to encompass the common tacit knowledge of the software development community. Terminologies used in the business world can be assigned to our semantic network, so software developers do not miss features that are not specific to their world but relevant to their clients. We present an initial experiment of mining semantic network from Stack Overflow and provide insights of the newly captured relations compared to WordNet.
{"title":"Mining Hypernyms Semantic Relations from Stack Overflow","authors":"L. Tóth, Balázs Nagy, T. Gyimóthy, László Vidács","doi":"10.1145/3387940.3392160","DOIUrl":"https://doi.org/10.1145/3387940.3392160","url":null,"abstract":"Communication between a software development team and business partners is often a challenging task due to the different context of terms used in the information exchange. The various contexts in which the concepts are defined or used create slightly different semantic fields that can evolve into information and communication silos. Due to the silo effect, the necessary information is often inadequately forwarded to developers resulting in poorly specified software requirements or misinterpreted user feedback. Communication difficulties can be reduced by introducing a mapping between the semantic fields of the parties involved in the communication based on the commonly used terminologies. Our research aims to obtain a suitable semantic database in the form of a semantic network built from the Stack Overflow corpus, which can be considered to encompass the common tacit knowledge of the software development community. Terminologies used in the business world can be assigned to our semantic network, so software developers do not miss features that are not specific to their world but relevant to their clients. We present an initial experiment of mining semantic network from Stack Overflow and provide insights of the newly captured relations compared to WordNet.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122521792","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}
Docker helps developers reuse software artifacts by providing a lightweight solution to the problem of operating system virtualization. A Docker image contains very rich and useful knowledge of software engineering, including the source of software packages, the correlations among software packages, the installation methods of software packages and the information on operating systems. To effectively obtain this knowledge, this paper proposes an approach to constructing a knowledge graph of Docker artifacts, named DockerKG, by analyzing a large number of Dockerfiles in Docker Hub, which contains more than 3.08 million Docker repositories (up to February 2020). Currently, DockerKG contains the domain knowledge extracted from approximately 200 thousand Dockerfiles in Docker Hub. Besides, it contains the information on Docker repositories and their semantic tags. In future work, DockerKG can be used for Docker image recommendations and online Q&A service providing software engineering domain knowledge.
{"title":"DockerKG","authors":"Jiahong Zhou, Wei Chen, Chang Liu, Jiaxin Zhu, Guoquan Wu, Jun Wei","doi":"10.1145/3387940.3392161","DOIUrl":"https://doi.org/10.1145/3387940.3392161","url":null,"abstract":"Docker helps developers reuse software artifacts by providing a lightweight solution to the problem of operating system virtualization. A Docker image contains very rich and useful knowledge of software engineering, including the source of software packages, the correlations among software packages, the installation methods of software packages and the information on operating systems. To effectively obtain this knowledge, this paper proposes an approach to constructing a knowledge graph of Docker artifacts, named DockerKG, by analyzing a large number of Dockerfiles in Docker Hub, which contains more than 3.08 million Docker repositories (up to February 2020). Currently, DockerKG contains the domain knowledge extracted from approximately 200 thousand Dockerfiles in Docker Hub. Besides, it contains the information on Docker repositories and their semantic tags. In future work, DockerKG can be used for Docker image recommendations and online Q&A service providing software engineering domain knowledge.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"10482 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122596484","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 "dialogue act" is a written or spoken action during a conversation. Dialogue acts are usually only a few words long, and are often categorized by researchers into a relatively small set of dialogue act types, such as eliciting information, expressing an opinion, or making a greeting. Research interest into automatic classification of dialogue acts has grown recently due to the proliferation of Virtual Agents (VA) e.g. Siri, Cortana, Alexa. But unfortunately, the gains made into VA development in one domain are generally not applicable to other domains, since the composition of dialogue acts differs in different conversations. In this paper, we target the problem of dialogue act classification for a VA for software engineers repairing bugs. A problem in the SE domain is that very little sample data exists - the only public dataset is a recently-released Wizard of Oz study with 30 conversations. Therefore, we present a transfer-learning technique to learn on a much larger dataset for general business conversations, and apply the knowledge to the SE dataset. In an experiment, we observe between 8% and 20% improvement over two key baselines.
{"title":"Dialogue Act Classification for Virtual Agents for Software Engineers during Debugging","authors":"Andrew Wood, Zachary Eberhart, Collin McMillan","doi":"10.1145/3387940.3391487","DOIUrl":"https://doi.org/10.1145/3387940.3391487","url":null,"abstract":"A \"dialogue act\" is a written or spoken action during a conversation. Dialogue acts are usually only a few words long, and are often categorized by researchers into a relatively small set of dialogue act types, such as eliciting information, expressing an opinion, or making a greeting. Research interest into automatic classification of dialogue acts has grown recently due to the proliferation of Virtual Agents (VA) e.g. Siri, Cortana, Alexa. But unfortunately, the gains made into VA development in one domain are generally not applicable to other domains, since the composition of dialogue acts differs in different conversations. In this paper, we target the problem of dialogue act classification for a VA for software engineers repairing bugs. A problem in the SE domain is that very little sample data exists - the only public dataset is a recently-released Wizard of Oz study with 30 conversations. Therefore, we present a transfer-learning technique to learn on a much larger dataset for general business conversations, and apply the knowledge to the SE dataset. In an experiment, we observe between 8% and 20% improvement over two key baselines.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117080965","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}
J. Klünder, Nils Prenner, Ann-Kathrin Windmann, Marek Stess, Michael Nolting, Fabian Kortum, Lisa Handke, K. Schneider, S. Kauffeld
Software development is a very cooperative and communicative task. In most software projects, meetings are a very important medium to share information. However, these meetings are often not as effective as expected. One big issue hindering productive and satisfying meetings is inappropriate behavior such as complaining. In particular, talking about problems without at least trying to solve them decreases motivation and mood of the team. Interaction analyses in meetings allow the assessment of appropriate and inappropriate behavior influencing the quality of a meeting. Derived from an established interaction analysis coding scheme in psychology, we present act4teams-short which allows real-time coding of meetings in software projects. We apply act4teams-short in an industrial case study at Volkswagen Commercial Vehicles, a large German company in the automotive domain. We analyze ten team-internal meetings at early project stages. Our results reveal difficulties due to missing project structure and the overall project goal. Furthermore, the team has an intrinsic interest in identifying problems and solving them, without any extrinsic input being required.
{"title":"Do You Just Discuss or Do You Solve?: Meeting Analysis in a Software Project at Early Stages","authors":"J. Klünder, Nils Prenner, Ann-Kathrin Windmann, Marek Stess, Michael Nolting, Fabian Kortum, Lisa Handke, K. Schneider, S. Kauffeld","doi":"10.1145/3387940.3391468","DOIUrl":"https://doi.org/10.1145/3387940.3391468","url":null,"abstract":"Software development is a very cooperative and communicative task. In most software projects, meetings are a very important medium to share information. However, these meetings are often not as effective as expected. One big issue hindering productive and satisfying meetings is inappropriate behavior such as complaining. In particular, talking about problems without at least trying to solve them decreases motivation and mood of the team. Interaction analyses in meetings allow the assessment of appropriate and inappropriate behavior influencing the quality of a meeting. Derived from an established interaction analysis coding scheme in psychology, we present act4teams-short which allows real-time coding of meetings in software projects. We apply act4teams-short in an industrial case study at Volkswagen Commercial Vehicles, a large German company in the automotive domain. We analyze ten team-internal meetings at early project stages. Our results reveal difficulties due to missing project structure and the overall project goal. Furthermore, the team has an intrinsic interest in identifying problems and solving them, without any extrinsic input being required.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124602116","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}
Pub Date : 2020-06-27DOI: 10.5040/9781350040809.00000002
Jón Arnar Briem, Jordi Smit, Hendrig Sellik, Pavel Rapoport, Georgios Gousios, M. Aniche
{"title":"OffSide","authors":"Jón Arnar Briem, Jordi Smit, Hendrig Sellik, Pavel Rapoport, Georgios Gousios, M. Aniche","doi":"10.5040/9781350040809.00000002","DOIUrl":"https://doi.org/10.5040/9781350040809.00000002","url":null,"abstract":"","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125629531","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}
Code recommendation systems for software engineering are designed to accelerate the development of large software projects. A classical example is code completion or next token prediction offered by modern integrated development environments. A particular challenging case for such systems are dynamic languages like Python due to limited type information at editing time. Recently, researchers proposed machine learning approaches to address this challenge. In particular, the Probabilistic Higher Order Grammar technique (Bielik et al., ICML 2016) uses a grammar-based approach with a classical machine learning schema to exploit local context. A method by Li et al., (IJCAI 2018) uses deep learning methods, in detail a Recurrent Neural Network coupled with a Pointer Network. We compare these two approaches quantitatively on a large corpus of Python files from GitHub. We also propose a combination of both approaches, where a neural network decides which schema to use for each prediction. The proposed method achieves a slightly better accuracy than either of the systems alone. This demonstrates the potential of ensemble-like methods for code completion and recommendation tasks in dynamically typed languages.
{"title":"Improving Code Recommendations by Combining Neural and Classical Machine Learning Approaches","authors":"M. Schumacher, K. T. Le, A. Andrzejak","doi":"10.1145/3387940.3391489","DOIUrl":"https://doi.org/10.1145/3387940.3391489","url":null,"abstract":"Code recommendation systems for software engineering are designed to accelerate the development of large software projects. A classical example is code completion or next token prediction offered by modern integrated development environments. A particular challenging case for such systems are dynamic languages like Python due to limited type information at editing time. Recently, researchers proposed machine learning approaches to address this challenge. In particular, the Probabilistic Higher Order Grammar technique (Bielik et al., ICML 2016) uses a grammar-based approach with a classical machine learning schema to exploit local context. A method by Li et al., (IJCAI 2018) uses deep learning methods, in detail a Recurrent Neural Network coupled with a Pointer Network. We compare these two approaches quantitatively on a large corpus of Python files from GitHub. We also propose a combination of both approaches, where a neural network decides which schema to use for each prediction. The proposed method achieves a slightly better accuracy than either of the systems alone. This demonstrates the potential of ensemble-like methods for code completion and recommendation tasks in dynamically typed languages.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131324260","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 advancements in the field of internet of things, artificial intelligence, machine learning, and data analytics has laid the path to the evolution of digital twin technology. The digital twin is a high-fidelity digital model of a physical system or asset that can be used e.g. to optimize operations and predict faults of the physical system. To understand different use cases of digital twin and its potential for cybersecurity incident prediction, we have performed a Systematic Literature Review (SLR). In this paper, we summarize the definition of digital twin and state-of-the-art on the development of digital twin including reported work on the usability of a digital twin for cybersecurity. Existing tools and technologies for developing digital twin is discussed.
{"title":"Digital Twin for Cybersecurity Incident Prediction: A Multivocal Literature Review","authors":"Abhishek Pokhrel, Vikash Katta, Ricardo Colomo Palacios","doi":"10.1145/3387940.3392199","DOIUrl":"https://doi.org/10.1145/3387940.3392199","url":null,"abstract":"The advancements in the field of internet of things, artificial intelligence, machine learning, and data analytics has laid the path to the evolution of digital twin technology. The digital twin is a high-fidelity digital model of a physical system or asset that can be used e.g. to optimize operations and predict faults of the physical system. To understand different use cases of digital twin and its potential for cybersecurity incident prediction, we have performed a Systematic Literature Review (SLR). In this paper, we summarize the definition of digital twin and state-of-the-art on the development of digital twin including reported work on the usability of a digital twin for cybersecurity. Existing tools and technologies for developing digital twin is discussed.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127670670","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}
E. Winter, David Bowes, S. Counsell, T. Hall, Saemundur O. Haraldsson, Vesna Nowack, J. Woodward
Automatic software repair represents a significant development in software engineering, promising considerable potential change to the working procedures and practices of software engineers. Technical advances have been the focus of many recent publications. However, there has not been an equivalent growth of studies of human factors within automatic software repair. This position paper presents the case for increased research in this area and suggests three key focuses and approaches for a future research agenda. All three of these enable industry-based software engineers not just to provide feedback on automatic software repair tools but to participate in shaping these technologies so that they meet developer and industry needs.
{"title":"Human Factors in the Study of Automatic Software Repair: Future Directions for Research with Industry","authors":"E. Winter, David Bowes, S. Counsell, T. Hall, Saemundur O. Haraldsson, Vesna Nowack, J. Woodward","doi":"10.1145/3387940.3392176","DOIUrl":"https://doi.org/10.1145/3387940.3392176","url":null,"abstract":"Automatic software repair represents a significant development in software engineering, promising considerable potential change to the working procedures and practices of software engineers. Technical advances have been the focus of many recent publications. However, there has not been an equivalent growth of studies of human factors within automatic software repair. This position paper presents the case for increased research in this area and suggests three key focuses and approaches for a future research agenda. All three of these enable industry-based software engineers not just to provide feedback on automatic software repair tools but to participate in shaping these technologies so that they meet developer and industry needs.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114607434","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}
Alex Bogart, E. Alomar, Mohamed Wiem Mkaouer, Ali Ouni
In software development, maintaining good design is essential. The process of refactoring enables developers to improve this design during development without altering the program's existing behavior. However, this process can be time-consuming, introduce semantic errors, and be difficult for developers inexperienced with refactoring or unfamiliar with a given code base. Automated refactoring tools can help not only by applying these changes, but by identifying opportunities for refactoring. Yet, developers have not been quick to adopt these tools due to a lack of trust between the developer and the tool. We propose an approach in the form of a visualization to aid developers in understanding these suggested operations and increasing familiarity with automated refactoring tools. We also provide a manual validation of this approach and identify options to continue experimentation.
{"title":"Increasing the Trust In Refactoring Through Visualization","authors":"Alex Bogart, E. Alomar, Mohamed Wiem Mkaouer, Ali Ouni","doi":"10.1145/3387940.3392190","DOIUrl":"https://doi.org/10.1145/3387940.3392190","url":null,"abstract":"In software development, maintaining good design is essential. The process of refactoring enables developers to improve this design during development without altering the program's existing behavior. However, this process can be time-consuming, introduce semantic errors, and be difficult for developers inexperienced with refactoring or unfamiliar with a given code base. Automated refactoring tools can help not only by applying these changes, but by identifying opportunities for refactoring. Yet, developers have not been quick to adopt these tools due to a lack of trust between the developer and the tool. We propose an approach in the form of a visualization to aid developers in understanding these suggested operations and increasing familiarity with automated refactoring tools. We also provide a manual validation of this approach and identify options to continue experimentation.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123948075","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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