A major hurdle for students and professional software developers who want to enter the world of machine learning (ML), is mastering not just the scientific background but also the available ML APIs, Therefore, we address the challenge of creating APIs that are easy to learn and use, especially by novices. However, it is not clear how this can be achieved without compromising expressiveness. We investigate this problem for scikit-learn, a widely used ML API. In this paper, we analyze its use by the Kaggle community, identifying unused and apparently useless parts of the API that can be eliminated without affecting client programs. In addition, we discuss usability issues in the remaining parts, propose related design improvements and show how they can be implemented by semi-automated wrapping of the existing third-party API. CCS CONCEPTS• Software and its engineering → Software libraries and repositories; • Computing methodologies → Machine learning.
{"title":"Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping","authors":"Lars Reimann, Günter Kniesel-Wünsche","doi":"10.1145/3510455.3512789","DOIUrl":"https://doi.org/10.1145/3510455.3512789","url":null,"abstract":"A major hurdle for students and professional software developers who want to enter the world of machine learning (ML), is mastering not just the scientific background but also the available ML APIs, Therefore, we address the challenge of creating APIs that are easy to learn and use, especially by novices. However, it is not clear how this can be achieved without compromising expressiveness. We investigate this problem for scikit-learn, a widely used ML API. In this paper, we analyze its use by the Kaggle community, identifying unused and apparently useless parts of the API that can be eliminated without affecting client programs. In addition, we discuss usability issues in the remaining parts, propose related design improvements and show how they can be implemented by semi-automated wrapping of the existing third-party API. CCS CONCEPTS• Software and its engineering → Software libraries and repositories; • Computing methodologies → Machine learning.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130845901","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}
P. Matsubara, Igor Steinmacher, B. Gadelha, T. Conte
Software estimation is critical for a software project’s success and a challenging activity. We argue that estimation problems are not restricted to the generation of estimates but also their use for commitment establishment: project stakeholders pressure estimators to change their estimates or to accept unrealistic commitments to attain business goals. In this study, we employed a Design Science Research (DSR) methodology to design an artifact based on negotiation methods, to empower software estimators in defending their estimates and searching for alternatives to unrealistic commitments when facing pressure. The artifact is a concrete step towards disseminating the soft skill of negotiation among practitioners. We present the preliminary results from a focus group that showed that practitioners from the software industry could use the artifact in a concrete scenario when estimating and establishing commitments about a software project. Our future steps include improving the artifact with the suggestions from focus group participants and evaluating it empirically in real software projects in the industry. CCS CONCEPTS • Software and its engineering → Software development process management.
{"title":"The best defense is a good defense: adapting negotiation methods for tackling pressure over software project estimates","authors":"P. Matsubara, Igor Steinmacher, B. Gadelha, T. Conte","doi":"10.1145/3510455.3512775","DOIUrl":"https://doi.org/10.1145/3510455.3512775","url":null,"abstract":"Software estimation is critical for a software project’s success and a challenging activity. We argue that estimation problems are not restricted to the generation of estimates but also their use for commitment establishment: project stakeholders pressure estimators to change their estimates or to accept unrealistic commitments to attain business goals. In this study, we employed a Design Science Research (DSR) methodology to design an artifact based on negotiation methods, to empower software estimators in defending their estimates and searching for alternatives to unrealistic commitments when facing pressure. The artifact is a concrete step towards disseminating the soft skill of negotiation among practitioners. We present the preliminary results from a focus group that showed that practitioners from the software industry could use the artifact in a concrete scenario when estimating and establishing commitments about a software project. Our future steps include improving the artifact with the suggestions from focus group participants and evaluating it empirically in real software projects in the industry. CCS CONCEPTS • Software and its engineering → Software development process management.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131313831","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}
Open source software (OSS) development relies on diverse skill sets. However, to our knowledge, there are no tools which detect OSSrelated skills. In this paper, we present a novel method to detect OSS skills and prototype it in a tool called DisKo. Our approach relies on identifying relevant signals, which are measurable activities or cues associated with a skill. Our tool detects how contributors 1) teach others to be involved in OSS projects, 2) show commitment towards an OSS project, 3) have knowledge in specific programming languages, and 4) are familiar with OSS practices. We then evaluate the tool by administering a survey to 455 OSS contributors. We demonstrate that DisKo yields promising results: it detects the presence of these skills with precision scores between 77% to 97%. We also find that over 54% of participants would display their high-proficiency skills. Our approach can be used to transform existing OSS experiences, such as identifying collaborators, matching mentors to mentees, and assigning project roles. Given the positive results and potential impact of our approach, we outline future research opportunities in interpreting and sharing OSS skills. CCS CONCEPTS • Software and its engineering → Open source model.
{"title":"Towards Mining OSS Skills from GitHub Activity","authors":"Jenny T Liang, Thomas Zimmermann, Denae Ford","doi":"10.1145/3510455.3512772","DOIUrl":"https://doi.org/10.1145/3510455.3512772","url":null,"abstract":"Open source software (OSS) development relies on diverse skill sets. However, to our knowledge, there are no tools which detect OSSrelated skills. In this paper, we present a novel method to detect OSS skills and prototype it in a tool called DisKo. Our approach relies on identifying relevant signals, which are measurable activities or cues associated with a skill. Our tool detects how contributors 1) teach others to be involved in OSS projects, 2) show commitment towards an OSS project, 3) have knowledge in specific programming languages, and 4) are familiar with OSS practices. We then evaluate the tool by administering a survey to 455 OSS contributors. We demonstrate that DisKo yields promising results: it detects the presence of these skills with precision scores between 77% to 97%. We also find that over 54% of participants would display their high-proficiency skills. Our approach can be used to transform existing OSS experiences, such as identifying collaborators, matching mentors to mentees, and assigning project roles. Given the positive results and potential impact of our approach, we outline future research opportunities in interpreting and sharing OSS skills. CCS CONCEPTS • Software and its engineering → Open source model.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126190799","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}
We consider a new approach to generate tests from natural language. Rather than relying on machine learning or templated extraction from structured comments, we propose to apply classic ideas from linguistics to translate natural-language sentences into executable tests. This paper explores the application of combinatory categorial grammars (CCGs) to generating property-based tests. Our prototype is able to generate tests from English descriptions for each example in a textbook chapter on property-based testing.
{"title":"Towards Property-Based Tests in Natural Language","authors":"Colin S. Gordon","doi":"10.1145/3510455.3512781","DOIUrl":"https://doi.org/10.1145/3510455.3512781","url":null,"abstract":"We consider a new approach to generate tests from natural language. Rather than relying on machine learning or templated extraction from structured comments, we propose to apply classic ideas from linguistics to translate natural-language sentences into executable tests. This paper explores the application of combinatory categorial grammars (CCGs) to generating property-based tests. Our prototype is able to generate tests from English descriptions for each example in a textbook chapter on property-based testing.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129636571","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}
Ad hoc parsers are everywhere: they appear any time a string is split, looped over, interpreted, transformed, or otherwise processed. Every ad hoc parser gives rise to a language: the possibly infinite set of input strings that the program accepts without going wrong. Any language can be described by a formal grammar: a finite set of rules that can generate all strings of that language. But programmers do not write grammars for ad hoc parsers-even though they would be eminently useful. Grammars can serve as documentation, aid program comprehension, generate test inputs, and allow reasoning about language-theoretic security. We propose an automatic grammar inference system for ad hoc parsers that would enable all of these use cases, in addition to opening up new possibilities in mining software repositories and bi-directional parser synthesis.
{"title":"Grammars for Free: Toward Grammar Inference for Ad Hoc Parsers","authors":"M. Schröder, Jürgen Cito","doi":"10.1145/3510455.3512787","DOIUrl":"https://doi.org/10.1145/3510455.3512787","url":null,"abstract":"Ad hoc parsers are everywhere: they appear any time a string is split, looped over, interpreted, transformed, or otherwise processed. Every ad hoc parser gives rise to a language: the possibly infinite set of input strings that the program accepts without going wrong. Any language can be described by a formal grammar: a finite set of rules that can generate all strings of that language. But programmers do not write grammars for ad hoc parsers-even though they would be eminently useful. Grammars can serve as documentation, aid program comprehension, generate test inputs, and allow reasoning about language-theoretic security. We propose an automatic grammar inference system for ad hoc parsers that would enable all of these use cases, in addition to opening up new possibilities in mining software repositories and bi-directional parser synthesis.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"44 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120816269","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}
By seamlessly integrating everyday objects and by changing the way we interact with our surroundings, Internet of Things (IoT) is drastically improving the life quality of households and enhancing the productivity of businesses. Given the unique IoT characteristics, IoT applications have emerged distinctively from the mainstream application types. Inspired by the outlook of a programmable world, we further foresee an IoT-native trend in designing, developing, deploying, and maintaining software systems. However, although the challenges of IoT software projects are frequently discussed, addressing those challenges are still in the “crossing the chasm” period. By participating in a few various IoT projects, we gradually distilled three fundamental principles for engineering IoT-native software systems, such as just enough, just in time, and just for “me”. These principles target the challenges that are associated with the most typical features of IoT environments, ranging from resource limits to technology heterogeneity of IoT devices. We expect this research to trigger dedicated efforts, techniques and theories for the topic IoT-native software engineering. CCS CONCEPTS • Software and its engineering → Development frameworks and environments; Distributed systems organizing principles;. Human-centered computing → Ubiquitous and mobile computing systems and tools. ACM Reference Format: Zheng Li and Rajiv Ranjan. 2022. Just Enough, Just in Time, Just for “Me”: Fundamental Principles for Engineering IoT-native Software Systems. In Ne$tau$v Ideas and Emerging Results (ICSE-NIER'22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3510455.3512785
{"title":"Just Enough, Just in Time, Just for “Me”: Fundamental Principles for Engineering IoT-native Software Systems","authors":"Zheng Li, R. Ranjan","doi":"10.1145/3510455.3512785","DOIUrl":"https://doi.org/10.1145/3510455.3512785","url":null,"abstract":"By seamlessly integrating everyday objects and by changing the way we interact with our surroundings, Internet of Things (IoT) is drastically improving the life quality of households and enhancing the productivity of businesses. Given the unique IoT characteristics, IoT applications have emerged distinctively from the mainstream application types. Inspired by the outlook of a programmable world, we further foresee an IoT-native trend in designing, developing, deploying, and maintaining software systems. However, although the challenges of IoT software projects are frequently discussed, addressing those challenges are still in the “crossing the chasm” period. By participating in a few various IoT projects, we gradually distilled three fundamental principles for engineering IoT-native software systems, such as just enough, just in time, and just for “me”. These principles target the challenges that are associated with the most typical features of IoT environments, ranging from resource limits to technology heterogeneity of IoT devices. We expect this research to trigger dedicated efforts, techniques and theories for the topic IoT-native software engineering. CCS CONCEPTS • Software and its engineering → Development frameworks and environments; Distributed systems organizing principles;. Human-centered computing → Ubiquitous and mobile computing systems and tools. ACM Reference Format: Zheng Li and Rajiv Ranjan. 2022. Just Enough, Just in Time, Just for “Me”: Fundamental Principles for Engineering IoT-native Software Systems. In Ne$tau$v Ideas and Emerging Results (ICSE-NIER'22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3510455.3512785","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133865684","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}
Philipp Haindl, Georg Buchgeher, Maqbool Khan, Bernhard Moser
With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture product and process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for context-specific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project. CCS CONCEPTS• Human-centered computing; • Computing methodologies →Artificial intelligence; • Software andits engineering;
{"title":"Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing","authors":"Philipp Haindl, Georg Buchgeher, Maqbool Khan, Bernhard Moser","doi":"10.1145/3510455.3512788","DOIUrl":"https://doi.org/10.1145/3510455.3512788","url":null,"abstract":"With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture product and process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for context-specific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project. CCS CONCEPTS• Human-centered computing; • Computing methodologies →Artificial intelligence; • Software andits engineering;","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133853761","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 progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a learning process in software engineering. Our vision articulates on the idea of leveraging multi-modal learning approaches to modeling the programming world. In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated through particular language constructs. In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs. We conducted a preliminary evaluation that shows gain of effectiveness of the models for code search using a simple joint-learning method and prompts us to further investigate our research vision.
{"title":"Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning","authors":"M. Weyssow, H. Sahraoui, Bang Liu","doi":"10.1145/3510455.3512771","DOIUrl":"https://doi.org/10.1145/3510455.3512771","url":null,"abstract":"The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approaches based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a learning process in software engineering. Our vision articulates on the idea of leveraging multi-modal learning approaches to modeling the programming world. In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated through particular language constructs. In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs. We conducted a preliminary evaluation that shows gain of effectiveness of the models for code search using a simple joint-learning method and prompts us to further investigate our research vision.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114973570","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}
Thanh-Dat Nguyen, Thanh Le-Cong, Thanh-Hung Nguyen, X. Le, Quyet-Thang Huynh
Graph Neural Networks (GNNs) have recently emerged as an effective framework for representing and analyzing graph-structured data. GNNs have been applied to many real-world problems such as knowledge graph analysis, social networks recommendation, and even COVID-19 detection and vaccine development. However, unlike other deep neural networks such as Feedforward Neural Networks (FFNNs), few verification and property inference techniques exist for GNNs. This is potentially due to dynamic behaviors of GNNs, which can take arbitrary graphs as input, whereas FFNNs which only take fixed size numerical vectors as inputs. This paper proposes GNN-Infer, an approach to analyze and infer properties of GNNs by extracting influential structures of the GNNs and then converting them into FFNNs. This allows us to leverage existing powerful FFNNs analyses to obtain results for the original GNNs. We discuss various designs of CNN-lnfer to ensure the scalability and accuracy of the conversions. We also illustrate CNN-Infer on a study case of node classification. We believe that CNN-Infer opens new research directions for understanding and analyzing GNNs. ACM Reference Format: Thanh-Dat Nguyen, Thanh Le-Cong, ThanhVu H. Nguyen, Xuan-Bach D. Le, and Quyet-Thang Huynh. 2022. Toward the Analysis of Graph Neural Networks. In New Ideas and Emerging Results (ICSE-NIER’22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3510455.3512780
图神经网络(gnn)最近作为表示和分析图结构数据的有效框架而出现。gnn已被应用于许多现实问题，如知识图谱分析、社交网络推荐，甚至COVID-19检测和疫苗开发。然而，与前馈神经网络(FFNNs)等其他深度神经网络不同，gnn的验证和属性推断技术很少。这可能是由于gnn的动态行为，它可以将任意图作为输入，而ffnn只能将固定大小的数值向量作为输入。本文提出了GNN-Infer，一种通过提取gnn的影响结构并将其转换为ffnn来分析和推断gnn属性的方法。这使我们能够利用现有强大的ffnn分析来获得原始gnn的结果。我们讨论了cnn - lnver的各种设计，以确保转换的可扩展性和准确性。我们还用一个节点分类的研究案例来说明CNN-Infer。我们相信CNN-Infer为理解和分析gnn开辟了新的研究方向。ACM参考格式:阮thanh - dat, Thanh Le-聪，Thanh vu H. Nguyen, Xuan-Bach D. Le, Quyet-Thang Huynh. 2022。论图神经网络的分析。新思想和新成果(ICSE-NIER ' 22)， 2022年5月21-29日，美国宾夕法尼亚州匹兹堡。ACM，纽约，美国，5页。https://doi.org/10.1145/3510455.3512780
{"title":"Toward the Analysis of Graph Neural Networks","authors":"Thanh-Dat Nguyen, Thanh Le-Cong, Thanh-Hung Nguyen, X. Le, Quyet-Thang Huynh","doi":"10.1145/3510455.3512780","DOIUrl":"https://doi.org/10.1145/3510455.3512780","url":null,"abstract":"Graph Neural Networks (GNNs) have recently emerged as an effective framework for representing and analyzing graph-structured data. GNNs have been applied to many real-world problems such as knowledge graph analysis, social networks recommendation, and even COVID-19 detection and vaccine development. However, unlike other deep neural networks such as Feedforward Neural Networks (FFNNs), few verification and property inference techniques exist for GNNs. This is potentially due to dynamic behaviors of GNNs, which can take arbitrary graphs as input, whereas FFNNs which only take fixed size numerical vectors as inputs. This paper proposes GNN-Infer, an approach to analyze and infer properties of GNNs by extracting influential structures of the GNNs and then converting them into FFNNs. This allows us to leverage existing powerful FFNNs analyses to obtain results for the original GNNs. We discuss various designs of CNN-lnfer to ensure the scalability and accuracy of the conversions. We also illustrate CNN-Infer on a study case of node classification. We believe that CNN-Infer opens new research directions for understanding and analyzing GNNs. ACM Reference Format: Thanh-Dat Nguyen, Thanh Le-Cong, ThanhVu H. Nguyen, Xuan-Bach D. Le, and Quyet-Thang Huynh. 2022. Toward the Analysis of Graph Neural Networks. In New Ideas and Emerging Results (ICSE-NIER’22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3510455.3512780","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129259019","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}
“If we make this change to our code, how will it impact our clients?” It is difficult for library maintainers to answer this simple—yet essential!—question when evolving their libraries. Library maintainers are constantly balancing between two opposing positions: make changes at the risk of breaking some of their clients, or avoid changes and maintain compatibility at the cost of immobility and growing technical debt. We argue that the lack of objective usage data and tool support leaves maintainers with their own subjective perception of their community to make these decisions.We introduce BreakBot, a bot that analyses the pull requests of Java libraries on GitHub to identify the breaking changes they introduce and their impact on client projects. Through static analysis of libraries and clients, it extracts and summarizes objective data that enrich the code review process by providing maintainers with the appropriate information to decide whether—and how—changes should be accepted, directly in the pull requests.
{"title":"BreakBot: Analyzing the Impact of Breaking Changes to Assist Library Evolution","authors":"Lina Ochoa, Thomas Degueule, Jean-Rémy Falleri","doi":"10.1145/3510455.3512783","DOIUrl":"https://doi.org/10.1145/3510455.3512783","url":null,"abstract":"“If we make this change to our code, how will it impact our clients?” It is difficult for library maintainers to answer this simple—yet essential!—question when evolving their libraries. Library maintainers are constantly balancing between two opposing positions: make changes at the risk of breaking some of their clients, or avoid changes and maintain compatibility at the cost of immobility and growing technical debt. We argue that the lack of objective usage data and tool support leaves maintainers with their own subjective perception of their community to make these decisions.We introduce BreakBot, a bot that analyses the pull requests of Java libraries on GitHub to identify the breaking changes they introduce and their impact on client projects. Through static analysis of libraries and clients, it extracts and summarizes objective data that enrich the code review process by providing maintainers with the appropriate information to decide whether—and how—changes should be accepted, directly in the pull requests.","PeriodicalId":416186,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125566766","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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