Pub Date : 2019-05-27DOI: 10.1109/ICSE-NIER.2019.00018
R. Couceiro, G. Duarte, J. Durães, J. Castelhano, C. Duarte, C. Teixeira, M. Castelo‐Branco, P. Carvalho, H. Madeira
This paper presents emergent experimental results showing that mental effort of programmers in code understanding tasks can be monitored through HRV (heart rate variability) using non-intrusive wearable devices. Results suggest that HRV is a good predictor for cognitive load when analyzing code and HRV results are consistent with the mental effort perceived by programmers using NASA-TLX. Furthermore, code complexity metrics do not correlate entirely with mental effort and do not seem a good indicator of the subjective perception of complexity felt by programmers. These first results are presented in the context of the project BASE-Biofeedback Augmented Software Engineering, which is briefly sketched, and proposes a radical neuroscience enabled approach to introduce biofeedback in software development
{"title":"Biofeedback Augmented Software Engineering: Monitoring of Programmers' Mental Effort","authors":"R. Couceiro, G. Duarte, J. Durães, J. Castelhano, C. Duarte, C. Teixeira, M. Castelo‐Branco, P. Carvalho, H. Madeira","doi":"10.1109/ICSE-NIER.2019.00018","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00018","url":null,"abstract":"This paper presents emergent experimental results showing that mental effort of programmers in code understanding tasks can be monitored through HRV (heart rate variability) using non-intrusive wearable devices. Results suggest that HRV is a good predictor for cognitive load when analyzing code and HRV results are consistent with the mental effort perceived by programmers using NASA-TLX. Furthermore, code complexity metrics do not correlate entirely with mental effort and do not seem a good indicator of the subjective perception of complexity felt by programmers. These first results are presented in the context of the project BASE-Biofeedback Augmented Software Engineering, which is briefly sketched, and proposes a radical neuroscience enabled approach to introduce biofeedback in software development","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121640254","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 : 2019-05-27DOI: 10.1109/ICSE-NIER.2019.00036
Tobias Dürschmid, Eunsuk Kang, D. Garlan
Implementing a solution for a design decision that precisely satisfies the trade-off between quality attributes can be extremely challenging. Further, typically quality attribute trade-offs are not represented as first-class entities in development artifacts. Hence, decisions might be sub-optimal and lack requirements traceability as well as changeability. We propose Trade-off-oriented Development (ToD), a new concept to automate the selection and integration of reusable implementations for a given design decision based on quality attribute trade-offs. Implementations that vary in quality attributes and that solve reoccurring design decisions are collected in a design decision library. Developers declaratively specify the quality attribute trade-off, which is then used to automatically select the best fitting implementation. We argue that thereby, software could satisfy the trade-offs more precisely, requirements are traceable and changeable, and advances in implementations automatically improve existing software.
{"title":"Trade-Off-Oriented Development: Making Quality Attribute Trade-Offs First-Class","authors":"Tobias Dürschmid, Eunsuk Kang, D. Garlan","doi":"10.1109/ICSE-NIER.2019.00036","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00036","url":null,"abstract":"Implementing a solution for a design decision that precisely satisfies the trade-off between quality attributes can be extremely challenging. Further, typically quality attribute trade-offs are not represented as first-class entities in development artifacts. Hence, decisions might be sub-optimal and lack requirements traceability as well as changeability. We propose Trade-off-oriented Development (ToD), a new concept to automate the selection and integration of reusable implementations for a given design decision based on quality attribute trade-offs. Implementations that vary in quality attributes and that solve reoccurring design decisions are collected in a design decision library. Developers declaratively specify the quality attribute trade-off, which is then used to automatically select the best fitting implementation. We argue that thereby, software could satisfy the trade-offs more precisely, requirements are traceable and changeable, and advances in implementations automatically improve existing software.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128351203","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 : 2019-05-27DOI: 10.1109/ICSE-NIER.2019.00014
Matthew Stephan
We present our new ideas on taking the first steps towards cultivating synergy between model-driven engineering (MDE), machine learning, and software clones. Specifically, we describe our vision in realizing a cognizant virtual software modeling assistant that uses the latter two to improve software design and MDE. Software engineering has benefited greatly from knowledge-based cognizant source code completion and assistance, but MDE has few and limited analogous capabilities. We outline our research directions by describing our vision for a prototype assistant that provides suggestions to modelers performing model creation or extension in the form of 1) complete models for insertion or guidance, and 2) granular single-step operations. These suggestions are derived by detecting clones of the in-progress model and existing domain, organizational, and exemplar models. We overview our envisioned workflow between modeler and assistant, and, using Simulink as an example, illustrate different manifestations including multiple overlays with percentages and employing variant elements.
{"title":"Towards a Cognizant Virtual Software Modeling Assistant using Model Clones","authors":"Matthew Stephan","doi":"10.1109/ICSE-NIER.2019.00014","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00014","url":null,"abstract":"We present our new ideas on taking the first steps towards cultivating synergy between model-driven engineering (MDE), machine learning, and software clones. Specifically, we describe our vision in realizing a cognizant virtual software modeling assistant that uses the latter two to improve software design and MDE. Software engineering has benefited greatly from knowledge-based cognizant source code completion and assistance, but MDE has few and limited analogous capabilities. We outline our research directions by describing our vision for a prototype assistant that provides suggestions to modelers performing model creation or extension in the form of 1) complete models for insertion or guidance, and 2) granular single-step operations. These suggestions are derived by detecting clones of the in-progress model and existing domain, organizational, and exemplar models. We overview our envisioned workflow between modeler and assistant, and, using Simulink as an example, illustrate different manifestations including multiple overlays with percentages and employing variant elements.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134421706","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 : 2019-05-27DOI: 10.1109/ICSE-NIER.2019.00016
R. Robbes, Andrea Janes
Many software engineering data sets, particularly those that demand manual labelling for classification, are necessarily small. As a consequence, several recent software engineering papers have cast doubt on the effectiveness of deep neural networks for classification tasks, when applied to these data sets. We provide initial evidence that recent advances in Natural Language Processing, that allow neural networks to leverage large amount of unlabelled data in a pre-training phase, can significantly improve performance.
{"title":"Leveraging Small Software Engineering Data Sets with Pre-Trained Neural Networks","authors":"R. Robbes, Andrea Janes","doi":"10.1109/ICSE-NIER.2019.00016","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00016","url":null,"abstract":"Many software engineering data sets, particularly those that demand manual labelling for classification, are necessarily small. As a consequence, several recent software engineering papers have cast doubt on the effectiveness of deep neural networks for classification tasks, when applied to these data sets. We provide initial evidence that recent advances in Natural Language Processing, that allow neural networks to leverage large amount of unlabelled data in a pre-training phase, can significantly improve performance.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127656409","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 : 2019-05-01DOI: 10.1109/ICSE-NIER.2019.00026
C. Monni, M. Pezzè
The ability of predicting failures before their occurrence is a fundamental enabler for reducing field failures and improving the reliability of complex software systems. Recent research proposes many techniques to detect anomalous values of system metrics, and demonstrates that collective anomalies are a good symptom of failure-prone states. In this paper (i) we observe the analogy of complex software systems with multi-particle and network systems, (ii) propose to use energy-based models commonly exploited in physics and statistical mechanics to precisely reveal failure-prone behaviors without training with seeded errors, and (iii) present some preliminary experimental results that show the feasibility of our approach.
{"title":"Energy-Based Anomaly Detection A New Perspective for Predicting Software Failures","authors":"C. Monni, M. Pezzè","doi":"10.1109/ICSE-NIER.2019.00026","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00026","url":null,"abstract":"The ability of predicting failures before their occurrence is a fundamental enabler for reducing field failures and improving the reliability of complex software systems. Recent research proposes many techniques to detect anomalous values of system metrics, and demonstrates that collective anomalies are a good symptom of failure-prone states. In this paper (i) we observe the analogy of complex software systems with multi-particle and network systems, (ii) propose to use energy-based models commonly exploited in physics and statistical mechanics to precisely reveal failure-prone behaviors without training with seeded errors, and (iii) present some preliminary experimental results that show the feasibility of our approach.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115460290","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 : 2019-05-01DOI: 10.1109/icse-nier.2019.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icse-nier.2019.00003","DOIUrl":"https://doi.org/10.1109/icse-nier.2019.00003","url":null,"abstract":"","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124656776","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 : 2019-05-01DOI: 10.1109/ICSE-NIER.2019.00037
M. Aniche, J. Yoder, Fabio Kon
According to the extensive 50-year-old body of knowledge in object-oriented programming and design, good software designs are, among other characteristics, lowly coupled, highly cohesive, extensible, comprehensible, and not fragile. However, with the increased complexity and heterogeneity of contemporary software, this might not be enough. This paper discusses the practical challenges of object-oriented design in modern software development. We focus on three main challenges: (1) how technologies, frameworks, and architectures pressure developers to make design decisions that they would not take in an ideal scenario, (2) the complexity of current real-world problems require developers to devise not only a single, but several models for the same problem that live and interact together, and (3) how existing quality assessment techniques for object-oriented design should go beyond high-level metrics. Finally, we propose an agenda for future research that should be tackled by both scientists and practitioners soon. This paper is a call for arms for more reality-oriented research on the object-oriented software design field.
{"title":"Current Challenges in Practical Object-Oriented Software Design","authors":"M. Aniche, J. Yoder, Fabio Kon","doi":"10.1109/ICSE-NIER.2019.00037","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00037","url":null,"abstract":"According to the extensive 50-year-old body of knowledge in object-oriented programming and design, good software designs are, among other characteristics, lowly coupled, highly cohesive, extensible, comprehensible, and not fragile. However, with the increased complexity and heterogeneity of contemporary software, this might not be enough. This paper discusses the practical challenges of object-oriented design in modern software development. We focus on three main challenges: (1) how technologies, frameworks, and architectures pressure developers to make design decisions that they would not take in an ideal scenario, (2) the complexity of current real-world problems require developers to devise not only a single, but several models for the same problem that live and interact together, and (3) how existing quality assessment techniques for object-oriented design should go beyond high-level metrics. Finally, we propose an agenda for future research that should be tackled by both scientists and practitioners soon. This paper is a call for arms for more reality-oriented research on the object-oriented software design field.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123154527","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 : 2019-05-01DOI: 10.1109/ICSE-NIER.2019.00020
Munindar P. Singh, A. Chopra
This paper identifies similarities between requirements engineering (RE) and the scientific method. It argues that RE is concerned with the development of models of small universes and thus relates to natural science despite RE having a prescriptive rather than a descriptive nature. An explicitly philosophical stance sheds light on RE processes and brings up criteria for judging RE processes.
{"title":"Requirements Engineering as Science in the Small","authors":"Munindar P. Singh, A. Chopra","doi":"10.1109/ICSE-NIER.2019.00020","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00020","url":null,"abstract":"This paper identifies similarities between requirements engineering (RE) and the scientific method. It argues that RE is concerned with the development of models of small universes and thus relates to natural science despite RE having a prescriptive rather than a descriptive nature. An explicitly philosophical stance sheds light on RE processes and brings up criteria for judging RE processes.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133447499","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 : 2019-05-01DOI: 10.1109/ICSE-NIER.2019.00028
Catia Trubiani, Sven Apel
Uncertainty is particularly critical in software performance engineering when it relates to the values of important parameters such as workload, operational profile, and resource demand, because such parameters inevitably affect the overall system performance. Prior work focused on monitoring the performance characteristics of software systems while considering influence of configuration options. The problem of incorporating uncertainty as a first-class concept in the software development process to identify performance issues is still challenging. The PLUS (Performance Learning for Uncertainty of Software) approach aims at addressing these limitations by investigating the specification of a new class of performance models capturing how the different uncertainties underlying a software system affect its performance characteristics. The main goal of PLUS is to answer a fundamental question in the software performance engineering domain: How to model the variable configuration options (i.e., software and hardware resources) and their intrinsic uncertainties (e.g., resource demand, processor speed) to represent the performance characteristics of software systems? This way, software engineers are exposed to a quantitative evaluation of their systems that supports them in the task of identifying performance critical configurations along with their uncertainties.
{"title":"PLUS: Performance Learning for Uncertainty of Software","authors":"Catia Trubiani, Sven Apel","doi":"10.1109/ICSE-NIER.2019.00028","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00028","url":null,"abstract":"Uncertainty is particularly critical in software performance engineering when it relates to the values of important parameters such as workload, operational profile, and resource demand, because such parameters inevitably affect the overall system performance. Prior work focused on monitoring the performance characteristics of software systems while considering influence of configuration options. The problem of incorporating uncertainty as a first-class concept in the software development process to identify performance issues is still challenging. The PLUS (Performance Learning for Uncertainty of Software) approach aims at addressing these limitations by investigating the specification of a new class of performance models capturing how the different uncertainties underlying a software system affect its performance characteristics. The main goal of PLUS is to answer a fundamental question in the software performance engineering domain: How to model the variable configuration options (i.e., software and hardware resources) and their intrinsic uncertainties (e.g., resource demand, processor speed) to represent the performance characteristics of software systems? This way, software engineers are exposed to a quantitative evaluation of their systems that supports them in the task of identifying performance critical configurations along with their uncertainties.","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129851792","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 : 2019-05-01DOI: 10.1109/icse-nier.2019.00007
{"title":"Program Committee of ICSE-NIER 2019","authors":"","doi":"10.1109/icse-nier.2019.00007","DOIUrl":"https://doi.org/10.1109/icse-nier.2019.00007","url":null,"abstract":"","PeriodicalId":180082,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123024015","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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