Miller Trujillo, M. Linares-Vásquez, Camilo Escobar-Velásquez, Ivana Dusparic, Nicolás Cardozo
Deep Learning (DL) is powerful family of algorithms used for a wide variety of problems and systems, including safety critical systems. As a consequence, analyzing, understanding, and testing DL models is attracting more practitioners and researchers with the purpose of implementing DL systems that are robust, reliable, efficient, and accurate. First software testing approaches for DL systems have focused on black-box testing, white-box testing, and test cases generation, in particular for deep neural networks (CNNs and RNNs). However, Deep Reinforcement Learning (DRL), which is a branch of DL extending reinforcement learning, is still out of the scope of research providing testing techniques for DL systems. In this paper, we present a first step towards testing of DRL systems. In particular, we investigate whether neuron coverage (a widely used metric for white-box testing of DNNs) could be used also for DRL systems, by analyzing coverage evolutionary patterns, and the correlation with RL rewards.
{"title":"Does Neuron Coverage Matter for Deep Reinforcement Learning?: A Preliminary Study","authors":"Miller Trujillo, M. Linares-Vásquez, Camilo Escobar-Velásquez, Ivana Dusparic, Nicolás Cardozo","doi":"10.1145/3387940.3391462","DOIUrl":"https://doi.org/10.1145/3387940.3391462","url":null,"abstract":"Deep Learning (DL) is powerful family of algorithms used for a wide variety of problems and systems, including safety critical systems. As a consequence, analyzing, understanding, and testing DL models is attracting more practitioners and researchers with the purpose of implementing DL systems that are robust, reliable, efficient, and accurate. First software testing approaches for DL systems have focused on black-box testing, white-box testing, and test cases generation, in particular for deep neural networks (CNNs and RNNs). However, Deep Reinforcement Learning (DRL), which is a branch of DL extending reinforcement learning, is still out of the scope of research providing testing techniques for DL systems. In this paper, we present a first step towards testing of DRL systems. In particular, we investigate whether neuron coverage (a widely used metric for white-box testing of DNNs) could be used also for DRL systems, by analyzing coverage evolutionary patterns, and the correlation with RL rewards.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131612657","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}
Current research on the testing of machine translation software mainly focuses on functional correctness for valid, well-formed inputs. By contrast, robustness testing, which involves the ability of the software to handle erroneous or unanticipated inputs, is often overlooked. In this paper, we propose to address this important shortcoming. Using the metamorphic robustness testing approach, we compare the translations of original inputs with those of follow-up inputs having different categories of minor typos. Our empirical results reveal a lack of robustness in Google Translate, thereby opening a new research direction for the quality assurance of neural machine translators.
{"title":"Metamorphic Robustness Testing of Google Translate","authors":"Dickson T. S. Lee, Z. Zhou, T. H. Tse","doi":"10.1145/3387940.3391484","DOIUrl":"https://doi.org/10.1145/3387940.3391484","url":null,"abstract":"Current research on the testing of machine translation software mainly focuses on functional correctness for valid, well-formed inputs. By contrast, robustness testing, which involves the ability of the software to handle erroneous or unanticipated inputs, is often overlooked. In this paper, we propose to address this important shortcoming. Using the metamorphic robustness testing approach, we compare the translations of original inputs with those of follow-up inputs having different categories of minor typos. Our empirical results reveal a lack of robustness in Google Translate, thereby opening a new research direction for the quality assurance of neural machine translators.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134044997","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 robots, or bots, are useful for automating a wide variety of programming and software development tasks. Despite the advantages of using bots throughout the software engineering process, research shows that developers often face challenges interacting with these systems. To improve automated developer recommendations from bots, this work introduces developer recommendation choice architectures. Choice architecture is a behavioral science concept that suggests the presentation of options impacts the decisions humans make. To evaluate the impact of framing recommendations for software engineers, we examine the impact of one choice architecture, actionability, for improving the design of bot recommendations. We present the results of a preliminary study evaluating this choice architecture in a bot and provide implications for integrating choice architecture into the design of future software engineering bots.
{"title":"Sorry to Bother You Again: Developer Recommendation Choice Architectures for Designing Effective Bots","authors":"Chris Brown, Chris Parnin","doi":"10.1145/3387940.3391506","DOIUrl":"https://doi.org/10.1145/3387940.3391506","url":null,"abstract":"Software robots, or bots, are useful for automating a wide variety of programming and software development tasks. Despite the advantages of using bots throughout the software engineering process, research shows that developers often face challenges interacting with these systems. To improve automated developer recommendations from bots, this work introduces developer recommendation choice architectures. Choice architecture is a behavioral science concept that suggests the presentation of options impacts the decisions humans make. To evaluate the impact of framing recommendations for software engineers, we examine the impact of one choice architecture, actionability, for improving the design of bot recommendations. We present the results of a preliminary study evaluating this choice architecture in a bot and provide implications for integrating choice architecture into the design of future software engineering bots.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131732763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces Mutamorphic Relation for Machine Learning Testing. Mutamorphic Relation combines data mutation and metamorphic relations as test oracles for machine learning systems. These oracles can help achieve fully automatic testing as well as automatic repair of the machine learning models. The paper takes TransRepair as an example to show the effectiveness of Mutamorphic Relation in automatically testing and improving machine translators, TransRepair detects inconsistency bugs without access to human oracles. It then adopts probability-reference or cross-reference to post-process the translations, in a grey-box or black-box manner, to repair the inconsistencies. Manual inspection indicates that the translations repaired by TransRepair improve consistency in 87% of cases (degrading it in 2%), and that the repairs of have better translation acceptability in 27% of the cases (worse in 8%).
{"title":"Automatic Improvement of Machine Translation Using Mutamorphic Relation: Invited Talk Paper","authors":"Jie M. Zhang","doi":"10.1145/3387940.3391541","DOIUrl":"https://doi.org/10.1145/3387940.3391541","url":null,"abstract":"This paper introduces Mutamorphic Relation for Machine Learning Testing. Mutamorphic Relation combines data mutation and metamorphic relations as test oracles for machine learning systems. These oracles can help achieve fully automatic testing as well as automatic repair of the machine learning models. The paper takes TransRepair as an example to show the effectiveness of Mutamorphic Relation in automatically testing and improving machine translators, TransRepair detects inconsistency bugs without access to human oracles. It then adopts probability-reference or cross-reference to post-process the translations, in a grey-box or black-box manner, to repair the inconsistencies. Manual inspection indicates that the translations repaired by TransRepair improve consistency in 87% of cases (degrading it in 2%), and that the repairs of have better translation acceptability in 27% of the cases (worse in 8%).","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133152349","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}
Automated program repair (APR) is an emerging technique that can automatically generate patches for fixing bugs or vulnerabilities. To ensure correctness, the auto-generated patches are usually sent to developers for verification before applied in the program. To review patches, developers must figure out the root cause of a bug and understand the semantic impact of the patch, which is not straightforward and easy even for expert programmers. In this position paper, we envision an interactive patch suggestion approach that avoids such complex reasoning by instead enabling developers to review patches with a few clicks. We first automatically translate patch semantics into a set of what and how questions. Basically, the what questions formulate the expected program behaviors, while the how questions represent how to modify the program to realize the expected behaviors. We could leverage the existing APR technique to generate those questions and corresponding answers. Then, to evaluate the correctness of patches, developers just need to ask questions and click the corresponding answers.
{"title":"Interactive Patch Generation and Suggestion","authors":"Xiang Gao, Abhik Roychoudhury","doi":"10.1145/3387940.3392179","DOIUrl":"https://doi.org/10.1145/3387940.3392179","url":null,"abstract":"Automated program repair (APR) is an emerging technique that can automatically generate patches for fixing bugs or vulnerabilities. To ensure correctness, the auto-generated patches are usually sent to developers for verification before applied in the program. To review patches, developers must figure out the root cause of a bug and understand the semantic impact of the patch, which is not straightforward and easy even for expert programmers. In this position paper, we envision an interactive patch suggestion approach that avoids such complex reasoning by instead enabling developers to review patches with a few clicks. We first automatically translate patch semantics into a set of what and how questions. Basically, the what questions formulate the expected program behaviors, while the how questions represent how to modify the program to realize the expected behaviors. We could leverage the existing APR technique to generate those questions and corresponding answers. Then, to evaluate the correctness of patches, developers just need to ask questions and click the corresponding answers.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123537470","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}
Luis F. Rivera, H. Müller, Norha M. Villegas, Gabriel Tamura, Miguel A. Jiménez
Digital Twins (DT) are software systems representing different aspects of a physical or conceptual counterpart---the real twin, which is instrumented with several sensors or computing devices that generate, consume and transfer data to its DT with different purposes. In other words, DT systems are, to a large extent, IoT-intensive systems. Indeed, by exploiting and managing IoT data, artificial intelligence, and big data and simulation capabilities, DTs have emerged as a promising approach to manage the virtual manifestation of real-world entities throughout their entire lifecycle. Their proliferation will contribute to realizing the long-craved convergence of virtual and physical spaces to augment things and human capabilities. In this context, despite the proposal of noteworthy contributions, we argue that DTs have not been sufficiently investigated from a software engineering perspective. To address this, in this paper we propose GEMINIS, an architectural reference model that adopts self-adaptation, control, and model-driven engineering techniques to specify the structural and behavioural aspects of DTs and enable the evolution of their internal models. Moreover, we introduce an approach for engineering IoT-intensive Digital Twin Software Systems (DTSS) using GEMINIS' capabilities to deal with uncertain conditions that are inherent to the nature of mirrored physical environments and that might compromise the fidelity of a DT. With GEMINIS and the proposed approach, we aim to advance the engineering of DTSS as well as IoT and cyber-physical systems by providing practitioners with guidelines to model and specify inherent structural and behavioural characteristics of DTs, addressing common design concerns.
{"title":"On the Engineering of IoT-Intensive Digital Twin Software Systems","authors":"Luis F. Rivera, H. Müller, Norha M. Villegas, Gabriel Tamura, Miguel A. Jiménez","doi":"10.1145/3387940.3392195","DOIUrl":"https://doi.org/10.1145/3387940.3392195","url":null,"abstract":"Digital Twins (DT) are software systems representing different aspects of a physical or conceptual counterpart---the real twin, which is instrumented with several sensors or computing devices that generate, consume and transfer data to its DT with different purposes. In other words, DT systems are, to a large extent, IoT-intensive systems. Indeed, by exploiting and managing IoT data, artificial intelligence, and big data and simulation capabilities, DTs have emerged as a promising approach to manage the virtual manifestation of real-world entities throughout their entire lifecycle. Their proliferation will contribute to realizing the long-craved convergence of virtual and physical spaces to augment things and human capabilities. In this context, despite the proposal of noteworthy contributions, we argue that DTs have not been sufficiently investigated from a software engineering perspective. To address this, in this paper we propose GEMINIS, an architectural reference model that adopts self-adaptation, control, and model-driven engineering techniques to specify the structural and behavioural aspects of DTs and enable the evolution of their internal models. Moreover, we introduce an approach for engineering IoT-intensive Digital Twin Software Systems (DTSS) using GEMINIS' capabilities to deal with uncertain conditions that are inherent to the nature of mirrored physical environments and that might compromise the fidelity of a DT. With GEMINIS and the proposed approach, we aim to advance the engineering of DTSS as well as IoT and cyber-physical systems by providing practitioners with guidelines to model and specify inherent structural and behavioural characteristics of DTs, addressing common design concerns.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126801327","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}
Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that continuously change over time (e.g., emotional state of end-user). Due to these changes in the context, software continually needs to adapt for delivering software services that can satisfy user needs. However, to achieve this adaptation, it is important to gather and understand the user feedback. In this paper, we mainly investigate whether physiological data can be considered and used as a form of implicit user feedback. To this end, we conducted a case study involving a tourist traveling abroad, who used a wearable device for monitoring his physiological data, and a smartphone with a mobile app for reminding him to take his medication on time during four days. Through the case study, we were able to identify some factors and activities as emotional triggers, which were used for understanding the user context. Our results highlight the importance of having a context analyzer, which can help the system to determine whether the detected stress could be considered as actionable and consequently as implicit user feedback.
{"title":"Understanding Implicit User Feedback from Multisensorial and Physiological Data: A case study","authors":"Franci Suni Lopez, Nelly Condori-Fernández, Alejandro Catalá","doi":"10.1145/3387940.3391466","DOIUrl":"https://doi.org/10.1145/3387940.3391466","url":null,"abstract":"Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that continuously change over time (e.g., emotional state of end-user). Due to these changes in the context, software continually needs to adapt for delivering software services that can satisfy user needs. However, to achieve this adaptation, it is important to gather and understand the user feedback. In this paper, we mainly investigate whether physiological data can be considered and used as a form of implicit user feedback. To this end, we conducted a case study involving a tourist traveling abroad, who used a wearable device for monitoring his physiological data, and a smartphone with a mobile app for reminding him to take his medication on time during four days. Through the case study, we were able to identify some factors and activities as emotional triggers, which were used for understanding the user context. Our results highlight the importance of having a context analyzer, which can help the system to determine whether the detected stress could be considered as actionable and consequently as implicit user feedback.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128658475","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}
Darius Foo, Jonah Dela Cruz, S. Sekar, Asankhaya Sharma
The Scaled Agile Framework (SAFe) is a popular realisation of the agile methodology for large organisations. It is widely adopted but challenging to implement. We describe a new tool which automates aspects of the SAFe PI Planning process to enable continuous planning and facilitate collaboration between remote teams.
{"title":"Automating Continuous Planning in SAFe","authors":"Darius Foo, Jonah Dela Cruz, S. Sekar, Asankhaya Sharma","doi":"10.1145/3387940.3391536","DOIUrl":"https://doi.org/10.1145/3387940.3391536","url":null,"abstract":"The Scaled Agile Framework (SAFe) is a popular realisation of the agile methodology for large organisations. It is widely adopted but challenging to implement. We describe a new tool which automates aspects of the SAFe PI Planning process to enable continuous planning and facilitate collaboration between remote teams.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130680011","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 video game industry is a multimillionaire market, which makes solo indie developers millionaire in one day. However, success in the game industry is not a coincidence. Video game development is an unusual kind of software that mix multidisciplinary teams: software engineers, designers, and artists. Also, for a video game to become popular, it must be fun and polished: exhaustively well tested. Testing in video game development encompasses different types of tests at different moments of the development process. In particular, assessing the players' gameplay in a test session can drive the development drastically. The designers analyze the players' actions and behaviour in the game. They can then decide if a feature/level requires rework. They often spend many man/work hours reworking a feature just because it is not engaging. As the designers (usually) assess the gameplay session by hand, they cannot be sure that a specific feature is engaging enough. They would benefit from meaningful data that would help them better assess the gameplay and take the decision to keep, rework, or remove a feature. Consequently, we describe the need for an IoT framework to assess players' gameplay using IoT sensors together with game devices which will produce a rich output for the game designers.
{"title":"Improving Engagement Assessment in Gameplay Testing Sessions using IoT Sensors","authors":"Cristiano Politowski, Fábio Petrillo, Yann-Gaël Guéhéneuc","doi":"10.1145/3387940.3392249","DOIUrl":"https://doi.org/10.1145/3387940.3392249","url":null,"abstract":"The video game industry is a multimillionaire market, which makes solo indie developers millionaire in one day. However, success in the game industry is not a coincidence. Video game development is an unusual kind of software that mix multidisciplinary teams: software engineers, designers, and artists. Also, for a video game to become popular, it must be fun and polished: exhaustively well tested. Testing in video game development encompasses different types of tests at different moments of the development process. In particular, assessing the players' gameplay in a test session can drive the development drastically. The designers analyze the players' actions and behaviour in the game. They can then decide if a feature/level requires rework. They often spend many man/work hours reworking a feature just because it is not engaging. As the designers (usually) assess the gameplay session by hand, they cannot be sure that a specific feature is engaging enough. They would benefit from meaningful data that would help them better assess the gameplay and take the decision to keep, rework, or remove a feature. Consequently, we describe the need for an IoT framework to assess players' gameplay using IoT sensors together with game devices which will produce a rich output for the game designers.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130956227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This is essentially a 'call for research' and collaboration between industry and academia to improve the motivation and performance of software engineers through use of language, words and symbols. How languages and symbols shape the way people think, feel and behave has been a topic of wide research. Words have powerful association with perception and cognition and throughout history, language has been used as a medium for influencing minds and for mass propaganda. While this is widely understood in politics, psychology and sociology, very little research has been to study the implicit and explicit impact of words, phrases and language on the way software engineers think, feel, behave and perform. While software engineering could be seen as a science that lends itself to a formal process and methods, it can also be seen as a craft and art which needs imagination and creativity which in turn are influenced by emotions. We propose some hypotheses, research questions and ideas to trigger formal studies of deeper connections between language/ symbols and software engineers' performance. We also draw inspiration from a wide body of research already conducted in this area which have influenced the field of psychology, sociology and mass communication. This is essentially a 'call for research' and collaboration between industry and academia to improve the motivation and performance of software engineers through use of language, words and symbols.
{"title":"Research Idea on How Language and Symbols (Semantics and Semiotics) Affect Emotions of Software Engineers","authors":"Mahesh Venkataraman, Kishore P. Durg","doi":"10.1145/3387940.3392232","DOIUrl":"https://doi.org/10.1145/3387940.3392232","url":null,"abstract":"This is essentially a 'call for research' and collaboration between industry and academia to improve the motivation and performance of software engineers through use of language, words and symbols. How languages and symbols shape the way people think, feel and behave has been a topic of wide research. Words have powerful association with perception and cognition and throughout history, language has been used as a medium for influencing minds and for mass propaganda. While this is widely understood in politics, psychology and sociology, very little research has been to study the implicit and explicit impact of words, phrases and language on the way software engineers think, feel, behave and perform. While software engineering could be seen as a science that lends itself to a formal process and methods, it can also be seen as a craft and art which needs imagination and creativity which in turn are influenced by emotions. We propose some hypotheses, research questions and ideas to trigger formal studies of deeper connections between language/ symbols and software engineers' performance. We also draw inspiration from a wide body of research already conducted in this area which have influenced the field of psychology, sociology and mass communication. This is essentially a 'call for research' and collaboration between industry and academia to improve the motivation and performance of software engineers through use of language, words and symbols.","PeriodicalId":309659,"journal":{"name":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125582265","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：