Pub Date : 2019-05-01DOI: 10.1109/ICSE-NIER.2019.00027
G. Murphy
Software developers create amazing software that is constantly changing the world in which we live: Navigation systems make it easy to find hard to find locations, mobile phones help diagnose health conditions and communication with almost anyone anywhere is virtually effortless. To create these amazing systems, software developers use tooling that is stuck in the past. Integrated development environments enable tools to work together more seamlessly, but remain oriented around the static structure (i.e., files, classes, etc.) of software. This paper proposes that a focus on the study and implementation of context could enable software tooling to take a substantial step forward, helping software developers to work more effectively. We delve into initial ideas of what context in software development might be and how context might support creating tools that augment human intelligence, allowing developers to better focus on the complex problems they face as they build amazing software.
{"title":"Beyond Integrated Development Environments: Adding Context to Software Development","authors":"G. Murphy","doi":"10.1109/ICSE-NIER.2019.00027","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00027","url":null,"abstract":"Software developers create amazing software that is constantly changing the world in which we live: Navigation systems make it easy to find hard to find locations, mobile phones help diagnose health conditions and communication with almost anyone anywhere is virtually effortless. To create these amazing systems, software developers use tooling that is stuck in the past. Integrated development environments enable tools to work together more seamlessly, but remain oriented around the static structure (i.e., files, classes, etc.) of software. This paper proposes that a focus on the study and implementation of context could enable software tooling to take a substantial step forward, helping software developers to work more effectively. We delve into initial ideas of what context in software development might be and how context might support creating tools that augment human intelligence, allowing developers to better focus on the complex problems they face as they build amazing 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":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115021715","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.2019.00006
{"title":"Message from the NIER Chairs of ICSE 2019","authors":"","doi":"10.1109/icse-nier.2019.00006","DOIUrl":"https://doi.org/10.1109/icse-nier.2019.00006","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":"122546135","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.2019.00015
P. Runeson
Reliance on data for software systems engineering is increasing, e.g., to train machine learning applications. We foresee increasing costs for data collection and maintenance, leading to the risk of development budgets eaten up by commodity features, thus leaving little resources for differentiation and innovation. We therefore propose Open Collaborative Data (OCD) - a concept analogous to Open Source Software (OSS) - as a means to share data. In contrast to Open Data (OD), which e.g., governmental agencies provide to catalyze innovation, OCD is shared in open collaboration between commercial organizations, similar to OSS. To achieve this, there is a need for technical infrastructure (e.g., tools for version and access control), licence models, and governance models, all of which have to be tailored for data. However, as data may be sensitive for privacy, anonymization and obfuscation of data is also a research challenge. In this paper, we define the concept of Open Collaborative Data, demonstrate it by map data and image recognition examples, and outline a research agenda for OCD in software engineering as a basis for more efficient evolution of software systems.
{"title":"Open Collaborative Data - using OSS Principles to Share Data in SW Engineering","authors":"P. Runeson","doi":"10.1109/ICSE-NIER.2019.00015","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00015","url":null,"abstract":"Reliance on data for software systems engineering is increasing, e.g., to train machine learning applications. We foresee increasing costs for data collection and maintenance, leading to the risk of development budgets eaten up by commodity features, thus leaving little resources for differentiation and innovation. We therefore propose Open Collaborative Data (OCD) - a concept analogous to Open Source Software (OSS) - as a means to share data. In contrast to Open Data (OD), which e.g., governmental agencies provide to catalyze innovation, OCD is shared in open collaboration between commercial organizations, similar to OSS. To achieve this, there is a need for technical infrastructure (e.g., tools for version and access control), licence models, and governance models, all of which have to be tailored for data. However, as data may be sensitive for privacy, anonymization and obfuscation of data is also a research challenge. In this paper, we define the concept of Open Collaborative Data, demonstrate it by map data and image recognition examples, and outline a research agenda for OCD in software engineering as a basis for more efficient evolution of software systems.","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":"132477101","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/techdebt.2019.00005
hot in software are delivered experts in the field. DevOps, AIOps, automated vehicles, and games development. (3) An Artifacts Evaluation track that assesses whether artifacts resulting from ICSE 2019 research software tools or data) been packaged in way that facilitates replication, reuse, and repurposing. (4) A Student Mentoring Workshop (SMeW) for undergraduates and first- and second-year students aims to de-mystify PhD research and to promote graduate studies and research careers.
{"title":"Message from the ICSE 2019 General Chair","authors":"","doi":"10.1109/techdebt.2019.00005","DOIUrl":"https://doi.org/10.1109/techdebt.2019.00005","url":null,"abstract":"hot in software are delivered experts in the field. DevOps, AIOps, automated vehicles, and games development. (3) An Artifacts Evaluation track that assesses whether artifacts resulting from ICSE 2019 research software tools or data) been packaged in way that facilitates replication, reuse, and repurposing. (4) A Student Mentoring Workshop (SMeW) for undergraduates and first- and second-year students aims to de-mystify PhD research and to promote graduate studies and research careers.","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":"115430469","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.2019.00001
{"title":"[Title page i]","authors":"","doi":"10.1109/icse-nier.2019.00001","DOIUrl":"https://doi.org/10.1109/icse-nier.2019.00001","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":"124747000","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.2019.00031
Zenan Li, Xiaoxing Ma, Chang Xu, Chun Cao
There is a dramatically increasing interest in the quality assurance for DNN-based systems in the software engineering community. An emerging hot topic in this direction is structural coverage criteria for testing neural networks, which are inspired by coverage metrics used in conventional software testing. In this short paper, we argue that these criteria could be misleading because of the fundamental differences between neural networks and human written programs. Our preliminary exploration shows that (1) adversarial examples are pervasively distributed in the finely divided space defined by such coverage criteria, while available natural samples are very sparse, and as a consequence, (2) previously reported fault-detection "capabilities" conjectured from high coverage testing are more likely due to the adversary-oriented search but not the real "high" coverage.
{"title":"Structural Coverage Criteria for Neural Networks Could Be Misleading","authors":"Zenan Li, Xiaoxing Ma, Chang Xu, Chun Cao","doi":"10.1109/ICSE-NIER.2019.00031","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00031","url":null,"abstract":"There is a dramatically increasing interest in the quality assurance for DNN-based systems in the software engineering community. An emerging hot topic in this direction is structural coverage criteria for testing neural networks, which are inspired by coverage metrics used in conventional software testing. In this short paper, we argue that these criteria could be misleading because of the fundamental differences between neural networks and human written programs. Our preliminary exploration shows that (1) adversarial examples are pervasively distributed in the finely divided space defined by such coverage criteria, while available natural samples are very sparse, and as a consequence, (2) previously reported fault-detection \"capabilities\" conjectured from high coverage testing are more likely due to the adversary-oriented search but not the real \"high\" coverage.","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":"131934827","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 : 2019-02-17DOI: 10.1109/ICSE-NIER.2019.00030
Jasmine Sekhon, C. Fleming
The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually used. Deep neural networks lack an explicit control-flow structure, making it impossible to apply to them traditional software testing criteria such as code coverage. In this paper, we examine existing testing methods for deep neural networks, the opportunities for improvement and the need for a fast, scalable, generalizable end-to-end testing method. We also propose a coverage criterion for deep neural networks that tries to capture all possible parts of the deep neural network's logic.
{"title":"Towards Improved Testing For Deep Learning","authors":"Jasmine Sekhon, C. Fleming","doi":"10.1109/ICSE-NIER.2019.00030","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00030","url":null,"abstract":"The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually used. Deep neural networks lack an explicit control-flow structure, making it impossible to apply to them traditional software testing criteria such as code coverage. In this paper, we examine existing testing methods for deep neural networks, the opportunities for improvement and the need for a fast, scalable, generalizable end-to-end testing method. We also propose a coverage criterion for deep neural networks that tries to capture all possible parts of the deep neural network's logic.","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":"2019-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130200817","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 : 2019-02-16DOI: 10.1109/ICSE-NIER.2019.00025
J. H. Lutz, Neil Lutz, R. Lutz, Matthew R. Riley
Matter, especially DNA, is now programmed to carry out useful processes at the nanoscale. As these programs and processes become more complex and their envisioned safetycritical applications approach deployment, it is essential to develop methods for engineering trustworthiness into molecular programs. Some of this can be achieved by adapting existing software engineering methods, but molecular programming also presents new challenges that will require new methods. This paper presents a method for dealing with one such challenge, namely, the difficulty of ascertaining how robust a molecular program is to perturbations of the relative "clock speeds" of its various reactions. The method proposed here is gametheoretic. The robustness of a molecular program is quantified in terms of its ability to win (achieve its original objective) in games against other molecular programs that manipulate its relative clock speeds. This game-theoretic approach is general enough to quantify the security of a molecular program against malicious manipulations of its relative clock speeds. However, this preliminary report focuses on games against nature, games in which the molecular program's opponent perturbs clock speeds randomly (indifferently) according to the probabilities inherent in chemical kinetics.
{"title":"Robustness and Games against Nature in Molecular Programming","authors":"J. H. Lutz, Neil Lutz, R. Lutz, Matthew R. Riley","doi":"10.1109/ICSE-NIER.2019.00025","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00025","url":null,"abstract":"Matter, especially DNA, is now programmed to carry out useful processes at the nanoscale. As these programs and processes become more complex and their envisioned safetycritical applications approach deployment, it is essential to develop methods for engineering trustworthiness into molecular programs. Some of this can be achieved by adapting existing software engineering methods, but molecular programming also presents new challenges that will require new methods. This paper presents a method for dealing with one such challenge, namely, the difficulty of ascertaining how robust a molecular program is to perturbations of the relative \"clock speeds\" of its various reactions. The method proposed here is gametheoretic. The robustness of a molecular program is quantified in terms of its ability to win (achieve its original objective) in games against other molecular programs that manipulate its relative clock speeds. This game-theoretic approach is general enough to quantify the security of a molecular program against malicious manipulations of its relative clock speeds. However, this preliminary report focuses on games against nature, games in which the molecular program's opponent perturbs clock speeds randomly (indifferently) according to the probabilities inherent in chemical kinetics.","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":"2019-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131637646","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 : 2019-02-15DOI: 10.1109/ICSE-NIER.2019.00032
Ravi Mangal, A. Nori, A. Orso
Neural networks are becoming increasingly prevalent in software, and it is therefore important to be able to verify their behavior. Because verifying the correctness of neural networks is extremely challenging, it is common to focus on the verification of other properties of these systems. One important property, in particular, is robustness. Most existing definitions of robustness, however, focus on the worst-case scenario where the inputs are adversarial. Such notions of robustness are too strong, and unlikely to be satisfied by-and verifiable for-practical neural networks. Observing that real-world inputs to neural networks are drawn from non-adversarial probability distributions, we propose a novel notion of robustness: probabilistic robustness, which requires the neural network to be robust with at least (1 - ε) probability with respect to the input distribution. This probabilistic approach is practical and provides a principled way of estimating the robustness of a neural network. We also present an algorithm, based on abstract interpretation and importance sampling, for checking whether a neural network is probabilistically robust. Our algorithm uses abstract interpretation to approximate the behavior of a neural network and compute an overapproximation of the input regions that violate robustness. It then uses importance sampling to counter the effect of such overapproximation and compute an accurate estimate of the probability that the neural network violates the robustness property.
{"title":"Robustness of Neural Networks: A Probabilistic and Practical Approach","authors":"Ravi Mangal, A. Nori, A. Orso","doi":"10.1109/ICSE-NIER.2019.00032","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00032","url":null,"abstract":"Neural networks are becoming increasingly prevalent in software, and it is therefore important to be able to verify their behavior. Because verifying the correctness of neural networks is extremely challenging, it is common to focus on the verification of other properties of these systems. One important property, in particular, is robustness. Most existing definitions of robustness, however, focus on the worst-case scenario where the inputs are adversarial. Such notions of robustness are too strong, and unlikely to be satisfied by-and verifiable for-practical neural networks. Observing that real-world inputs to neural networks are drawn from non-adversarial probability distributions, we propose a novel notion of robustness: probabilistic robustness, which requires the neural network to be robust with at least (1 - ε) probability with respect to the input distribution. This probabilistic approach is practical and provides a principled way of estimating the robustness of a neural network. We also present an algorithm, based on abstract interpretation and importance sampling, for checking whether a neural network is probabilistically robust. Our algorithm uses abstract interpretation to approximate the behavior of a neural network and compute an overapproximation of the input regions that violate robustness. It then uses importance sampling to counter the effect of such overapproximation and compute an accurate estimate of the probability that the neural network violates the robustness property.","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":"2019-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115457979","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 : 2019-02-07DOI: 10.1109/ICSE-NIER.2019.00034
L. Cruz, Rui Abreu
Measuring energy consumption is a challenging task faced by developers when building mobile apps. This paper presents EMaaS: a system that provides reliable energy measurements for mobile applications, without requiring a complex setup. It combines estimations from an energy model with - typically more reliable, but also expensive - hardware-based measurements. On a per scenario basis, it decides whether the energy model is able to provide a reliable estimation of energy consumption. Otherwise, hardware-based measurements are provided. In addition, the system is accessible to the community of mobile software practitioners/researchers in the form of a Software as a Service. With this service, we aim at solving current problems in the field of energy efficiency in mobile software engineering: the complexity of hardware-based power monitor tools, the reliability of energy models, and the continuous need of data to build energy models.
{"title":"EMaaS: Energy Measurements as a Service for Mobile Applications","authors":"L. Cruz, Rui Abreu","doi":"10.1109/ICSE-NIER.2019.00034","DOIUrl":"https://doi.org/10.1109/ICSE-NIER.2019.00034","url":null,"abstract":"Measuring energy consumption is a challenging task faced by developers when building mobile apps. This paper presents EMaaS: a system that provides reliable energy measurements for mobile applications, without requiring a complex setup. It combines estimations from an energy model with - typically more reliable, but also expensive - hardware-based measurements. On a per scenario basis, it decides whether the energy model is able to provide a reliable estimation of energy consumption. Otherwise, hardware-based measurements are provided. In addition, the system is accessible to the community of mobile software practitioners/researchers in the form of a Software as a Service. With this service, we aim at solving current problems in the field of energy efficiency in mobile software engineering: the complexity of hardware-based power monitor tools, the reliability of energy models, and the continuous need of data to build energy models.","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":"2019-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127951075","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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