Susanna Ardigò, Csaba Nagy, Roberto Minelli, Michele Lanza
The city metaphor for visualizing software systems in 3D has been widely explored and has led to many diverse implementations and approaches. Common among all approaches is a focus on the software artifacts, while the aspects pertaining to the data and information (stored both in databases and files) used by a system are seldom taken into account.We present M3triCity, an interactive web application whose goal is to visualize object-oriented software systems, their evolution, and the way they access data and information. We illustrate how it can be used for program comprehension and evolution analysis of data-intensive software systems.Demo video URL: https://youtu.be/uBMvZFIlWtk
{"title":"M3triCity","authors":"Susanna Ardigò, Csaba Nagy, Roberto Minelli, Michele Lanza","doi":"10.1145/3510454.3516831","DOIUrl":"https://doi.org/10.1145/3510454.3516831","url":null,"abstract":"The city metaphor for visualizing software systems in 3D has been widely explored and has led to many diverse implementations and approaches. Common among all approaches is a focus on the software artifacts, while the aspects pertaining to the data and information (stored both in databases and files) used by a system are seldom taken into account.We present M3triCity, an interactive web application whose goal is to visualize object-oriented software systems, their evolution, and the way they access data and information. We illustrate how it can be used for program comprehension and evolution analysis of data-intensive software systems.Demo video URL: https://youtu.be/uBMvZFIlWtk","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114246711","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}
Point clouds have been widely used in a large number of application scenarios to handle with various deep learning (DL) tasks. Testing is an essential means to guarantee the robustness of DL models, which places high demands on test data. Therefore, it is crucial to design a reliable and effective test data augmentation tool of point clouds to generate high-quality data to test the robustness of the target model. However, although common mutation methods can increase the amount of point clouds, the quality of the augmented data still needs to be improved based on the specify of the spatial structure of the point clouds. In this paper, we develop a point clouds augmentation tool, namely TauPad, of which the specific mutation direction is guided by adversarial attacks. Based on the point clouds pre-processing, point clouds adversarial mutation, and spatial distribution restoration, TauPad can generate augmented test data that are significantly deceptive to the target model. Preliminary experiments show that TauPad can reliably and effectively augment point clouds for testing. Its video is at https://youtu.be/Y9nDIEW13_g/ and TauPad can be used at http://1.13.193.98:2600/.
{"title":"TauPad","authors":"Guandi Liu, Jiawei Liu, Quanjun Zhang, Chunrong Fang, Xufan Zhang","doi":"10.1145/3510454.3517050","DOIUrl":"https://doi.org/10.1145/3510454.3517050","url":null,"abstract":"Point clouds have been widely used in a large number of application scenarios to handle with various deep learning (DL) tasks. Testing is an essential means to guarantee the robustness of DL models, which places high demands on test data. Therefore, it is crucial to design a reliable and effective test data augmentation tool of point clouds to generate high-quality data to test the robustness of the target model. However, although common mutation methods can increase the amount of point clouds, the quality of the augmented data still needs to be improved based on the specify of the spatial structure of the point clouds. In this paper, we develop a point clouds augmentation tool, namely TauPad, of which the specific mutation direction is guided by adversarial attacks. Based on the point clouds pre-processing, point clouds adversarial mutation, and spatial distribution restoration, TauPad can generate augmented test data that are significantly deceptive to the target model. Preliminary experiments show that TauPad can reliably and effectively augment point clouds for testing. Its video is at https://youtu.be/Y9nDIEW13_g/ and TauPad can be used at http://1.13.193.98:2600/.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123753030","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 demonstrate the implementation and usage of SymInfer, a tool that automatically discovers numerical invariants using concrete and symbolic states collected from dynamic and symbolic executions. SymInfer supports expressive invariants under various forms, including nonlinear equalities, octagonal inequalities, and disjunctive min/max invariants. Experimental results show that SymInfer is effective in generating complex invariants and can often discover unknown, yet useful program properties. Video demo: https://www.youtube.com/watch?v=VEuhJw1RBUE.
{"title":"SymInfer","authors":"T. Nguyen, KimHao Nguyen, H. Duong","doi":"10.1145/3510454.3516833","DOIUrl":"https://doi.org/10.1145/3510454.3516833","url":null,"abstract":"We demonstrate the implementation and usage of SymInfer, a tool that automatically discovers numerical invariants using concrete and symbolic states collected from dynamic and symbolic executions. SymInfer supports expressive invariants under various forms, including nonlinear equalities, octagonal inequalities, and disjunctive min/max invariants. Experimental results show that SymInfer is effective in generating complex invariants and can often discover unknown, yet useful program properties. Video demo: https://www.youtube.com/watch?v=VEuhJw1RBUE.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124864654","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}
Hongzhou Fang, Yuanfang Cai, R. Kazman, J. Lefever
We present Cider, an operating system compatibility architecture that can run applications built for different mobile ecosystems, iOS or Android, together on the same smartphone or tablet. Cider enhances the domestic operating system, Android, of a device with kernel-managed, per-thread personas to mimic the application binary interface of a foreign operating system, iOS, enabling it to run unmodified foreign binaries. This is accomplished using a novel combination of binary compatibility techniques including two new mechanisms: compile-time code adaptation, and diplomatic functions. Compile-time code adaptation enables existing unmodified foreign source code to be reused in the domestic kernel, reducing implementation effort required to support multiple binary interfaces for executing domestic and foreign applications. Diplomatic functions leverage per-thread personas, and allow foreign applications to use domestic libraries to access proprietary software and hardware interfaces. We have built a Cider prototype, and demonstrate that it imposes modest performance overhead and runs unmodified iOS and Android applications together on a Google Nexus tablet running the latest version of Android.
{"title":"CIDER","authors":"Hongzhou Fang, Yuanfang Cai, R. Kazman, J. Lefever","doi":"10.1145/2541940.2541972","DOIUrl":"https://doi.org/10.1145/2541940.2541972","url":null,"abstract":"We present Cider, an operating system compatibility architecture that can run applications built for different mobile ecosystems, iOS or Android, together on the same smartphone or tablet. Cider enhances the domestic operating system, Android, of a device with kernel-managed, per-thread personas to mimic the application binary interface of a foreign operating system, iOS, enabling it to run unmodified foreign binaries. This is accomplished using a novel combination of binary compatibility techniques including two new mechanisms: compile-time code adaptation, and diplomatic functions. Compile-time code adaptation enables existing unmodified foreign source code to be reused in the domestic kernel, reducing implementation effort required to support multiple binary interfaces for executing domestic and foreign applications. Diplomatic functions leverage per-thread personas, and allow foreign applications to use domestic libraries to access proprietary software and hardware interfaces. We have built a Cider prototype, and demonstrate that it imposes modest performance overhead and runs unmodified iOS and Android applications together on a Google Nexus tablet running the latest version of Android.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114790073","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}
Automatic generation of requirements documents is an essential feature of the model-driven CASE tools such as UML and SysML designers. However, the quality of the generated documents from the current tools highly depends on the attached descriptions of models but not the quality of the model itself. Besides, if the stockholders ask to generate ISO/IEC/IEEE 29148-2018 conformed documents, extra templates are required. In this paper, we propose a CASE tool named RM2Doc, which can automatically generate ISO/IEC/IEEE 29148-2018 conformed requirements documents from UML models without any templates. In addition, the flow description can be generated from a use case without additional information. Moreover, it can automatically generate the semantic description of system operations only based on the formal expression of OCL. We have conducted four case studies with over 50 use cases. Overall, the result is satisfactory. The 95% requirements documents can be generated from the requirements model without any human interactions in 1 second. The proposed tools can be further developed for the industry of software engineering.The tool can be downloaded at http://rm2pt.com/rm2doc, and a demo video casting its features is at https://youtu.be/4z0Z5mrLfBc
{"title":"RM2Doc","authors":"Tianshu Bao, Jing Yang, Yilong Yang, Yongfeng Yin","doi":"10.1145/3510454.3516850","DOIUrl":"https://doi.org/10.1145/3510454.3516850","url":null,"abstract":"Automatic generation of requirements documents is an essential feature of the model-driven CASE tools such as UML and SysML designers. However, the quality of the generated documents from the current tools highly depends on the attached descriptions of models but not the quality of the model itself. Besides, if the stockholders ask to generate ISO/IEC/IEEE 29148-2018 conformed documents, extra templates are required. In this paper, we propose a CASE tool named RM2Doc, which can automatically generate ISO/IEC/IEEE 29148-2018 conformed requirements documents from UML models without any templates. In addition, the flow description can be generated from a use case without additional information. Moreover, it can automatically generate the semantic description of system operations only based on the formal expression of OCL. We have conducted four case studies with over 50 use cases. Overall, the result is satisfactory. The 95% requirements documents can be generated from the requirements model without any human interactions in 1 second. The proposed tools can be further developed for the industry of software engineering.The tool can be downloaded at http://rm2pt.com/rm2doc, and a demo video casting its features is at https://youtu.be/4z0Z5mrLfBc","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015058","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}
Jose María López-Morales, P. Cañizares, Sara Pérez-Soler, E. Guerra, J. de Lara
Chatbots have become a popular way to access all sorts of services via natural language. Many platforms and tools have been proposed for their construction, like Google’s Dialogflow, Amazon’s Lex or Rasa. However, most of them still miss integrated quality assurance methods like metrics. Moreover, there is currently a lack of mechanisms to compare and classify chatbots possibly developed with heterogeneous technologies.To tackle these issues, we present Asymob, a web platform that enables the measurement of chatbots using a suite of 20 metrics. The tool features a repository supporting chatbots built with different technologies, like Dialogflow and Rasa. Asymob’s metrics help in detecting quality issues and serve to compare chatbots across and within technologies. The tool also helps in classifying chatbots along conversation topics or design features by means of two clustering methods: based on the chatbot metrics or on the phrases expected and produced by the chatbot. A video showcasing the tool is available at https://www.youtube.com/watch?v=8lpETkILpv8.
{"title":"Asymob","authors":"Jose María López-Morales, P. Cañizares, Sara Pérez-Soler, E. Guerra, J. de Lara","doi":"10.1145/3510454.3516843","DOIUrl":"https://doi.org/10.1145/3510454.3516843","url":null,"abstract":"Chatbots have become a popular way to access all sorts of services via natural language. Many platforms and tools have been proposed for their construction, like Google’s Dialogflow, Amazon’s Lex or Rasa. However, most of them still miss integrated quality assurance methods like metrics. Moreover, there is currently a lack of mechanisms to compare and classify chatbots possibly developed with heterogeneous technologies.To tackle these issues, we present Asymob, a web platform that enables the measurement of chatbots using a suite of 20 metrics. The tool features a repository supporting chatbots built with different technologies, like Dialogflow and Rasa. Asymob’s metrics help in detecting quality issues and serve to compare chatbots across and within technologies. The tool also helps in classifying chatbots along conversation topics or design features by means of two clustering methods: based on the chatbot metrics or on the phrases expected and produced by the chatbot. A video showcasing the tool is available at https://www.youtube.com/watch?v=8lpETkILpv8.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127330282","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}
With the rapid development of object detection in deep learning (DL), applications on LiDAR point clouds have received much attention, such as autonomous driving. To verify the robustness of object detection models by testing, large amounts of diversifted annotated LiDAR point clouds are required to be used as test data. However, considering the sparseness of objects, the diversity of the existing point cloud dataset is limited by the number and types of objects. Therefore, it is important to generate diversifted point clouds by test data augmentation. In this paper, we propose a tool for LiDAR point cloud via test data augmentation, named TauLiM. A well-designed metamorphic relation (MR) [1] is proposed to augment point clouds while maintaining their physical characteristic of LiDAR. TauLiM is composed of three modules, namely point cloud configuration, coordinate filtering, and object insertion. To evaluate our tool, we employ experiments to compare the ability of testing between the existing dataset and the augmented one. The result shows that TauLiM can effectively augment diversified test data and test the object detection model. The video of TauLiM is available at https://www.youtube.com/watch?v=9S6xpRbbhtQ and TauLiM can be used at http://1.13.193.98:2601/.
{"title":"TauLiM","authors":"Justin Lin, Jiawei Liu, Quanjun Zhang, Xufan Zhang, Chunrong Fang","doi":"10.1145/3510454.3516860","DOIUrl":"https://doi.org/10.1145/3510454.3516860","url":null,"abstract":"With the rapid development of object detection in deep learning (DL), applications on LiDAR point clouds have received much attention, such as autonomous driving. To verify the robustness of object detection models by testing, large amounts of diversifted annotated LiDAR point clouds are required to be used as test data. However, considering the sparseness of objects, the diversity of the existing point cloud dataset is limited by the number and types of objects. Therefore, it is important to generate diversifted point clouds by test data augmentation. In this paper, we propose a tool for LiDAR point cloud via test data augmentation, named TauLiM. A well-designed metamorphic relation (MR) [1] is proposed to augment point clouds while maintaining their physical characteristic of LiDAR. TauLiM is composed of three modules, namely point cloud configuration, coordinate filtering, and object insertion. To evaluate our tool, we employ experiments to compare the ability of testing between the existing dataset and the augmented one. The result shows that TauLiM can effectively augment diversified test data and test the object detection model. The video of TauLiM is available at https://www.youtube.com/watch?v=9S6xpRbbhtQ and TauLiM can be used at http://1.13.193.98:2601/.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121793020","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}
Ya Xiao, Salman Ahmed, Xinyang Ge, Bimal Viswanath, Na Meng, D. Yao
In this paper, we conduct a measurement study to comprehensively compare the accuracy of Cryptographic API completion tasks trained with multiple API embedding options. Embedding is the process of automatically learning to represent program elements as low-dimensional vectors. Our measurement aims to uncover the impacts of applying program analysis, token-level embedding, and sequence-level embedding on the Cryptographic API completion accuracies. Our findings show that program analysis is necessary even under advanced embedding. The results show 36.10% accuracy improvement on average when program analysis preprocessing is applied to transfer byte code sequences into API dependence paths. The best accuracy (93.52%) is achieved on API dependence paths with embedding techniques. On the contrary, the pure data-driven approach without program analysis only achieves a low accuracy (around 57.60%), even after the powerful sequence-level embedding is applied. Although sequence-level embedding shows slight accuracy advantages (0.55% on average) over token-level embedding in our basic data split setting, it is not recommended considering its expensive training cost. A more obvious accuracy improvement (5.10%) from sequence-level embedding is observed under the cross-project learning scenario when task data is insufficient. Hence, we recommend applying sequence-level embedding for cross-project learning with limited task-specific data.
{"title":"Comprehensive comparisons of embedding approaches for cryptographic API completion: poster","authors":"Ya Xiao, Salman Ahmed, Xinyang Ge, Bimal Viswanath, Na Meng, D. Yao","doi":"10.1145/3510454.3528645","DOIUrl":"https://doi.org/10.1145/3510454.3528645","url":null,"abstract":"In this paper, we conduct a measurement study to comprehensively compare the accuracy of Cryptographic API completion tasks trained with multiple API embedding options. Embedding is the process of automatically learning to represent program elements as low-dimensional vectors. Our measurement aims to uncover the impacts of applying program analysis, token-level embedding, and sequence-level embedding on the Cryptographic API completion accuracies. Our findings show that program analysis is necessary even under advanced embedding. The results show 36.10% accuracy improvement on average when program analysis preprocessing is applied to transfer byte code sequences into API dependence paths. The best accuracy (93.52%) is achieved on API dependence paths with embedding techniques. On the contrary, the pure data-driven approach without program analysis only achieves a low accuracy (around 57.60%), even after the powerful sequence-level embedding is applied. Although sequence-level embedding shows slight accuracy advantages (0.55% on average) over token-level embedding in our basic data split setting, it is not recommended considering its expensive training cost. A more obvious accuracy improvement (5.10%) from sequence-level embedding is observed under the cross-project learning scenario when task data is insufficient. Hence, we recommend applying sequence-level embedding for cross-project learning with limited task-specific data.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692587","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}
Nader Al Awar, Neil B. Mehta, Steven Zhu, G. Biros, Miloš Gligorić
As modern supercomputers have increasingly heterogeneous hardware, the need for writing parallel code that is both portable and performant across different hardware architectures increases. Kokkos is a C++ library that provides abstractions for writing performance portable code. Using Kokkos, programmers can write their code once and run it efficiently on a variety of architectures. However, the target audience of Kokkos, typically scientists, prefers dynamically typed languages such as Python instead of C++. We demonstrate a framework, dubbed PyKokkos, that enables performance portable code through Python. PyKokkos transparently translates code written in a subset of Python to C++ and Kokkos, and then connects the generated code to Python by automatically generating language bindings. PyKokkos achieves performance comparable to Kokkos in ExaMiniMD, a ~3k lines of code molecular dynamics mini-application. The demo video for PyKokkos can be found at https://youtu.be/1oFvhlhoDaY.
{"title":"PyKokkos","authors":"Nader Al Awar, Neil B. Mehta, Steven Zhu, G. Biros, Miloš Gligorić","doi":"10.1145/3510454.3516827","DOIUrl":"https://doi.org/10.1145/3510454.3516827","url":null,"abstract":"As modern supercomputers have increasingly heterogeneous hardware, the need for writing parallel code that is both portable and performant across different hardware architectures increases. Kokkos is a C++ library that provides abstractions for writing performance portable code. Using Kokkos, programmers can write their code once and run it efficiently on a variety of architectures. However, the target audience of Kokkos, typically scientists, prefers dynamically typed languages such as Python instead of C++. We demonstrate a framework, dubbed PyKokkos, that enables performance portable code through Python. PyKokkos transparently translates code written in a subset of Python to C++ and Kokkos, and then connects the generated code to Python by automatically generating language bindings. PyKokkos achieves performance comparable to Kokkos in ExaMiniMD, a ~3k lines of code molecular dynamics mini-application. The demo video for PyKokkos can be found at https://youtu.be/1oFvhlhoDaY.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122055886","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}
Xiao Wang, L. Xiao, Tingting Yu, Anne Woepse, Sunny Wong
Mocking frameworks are dedicated to creating, manipulating, and verifying the execution of "faked" objects in unit testing. This helps developers to overcome the challenge of high inter-dependencies among software units. Despite the various benefits offered by existing mocking frameworks, developers often create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. We contribute a refactoring tool as an Eclipse Plugin, named JMocker, to automatically identify and replace the usage of inheritance by using Mockito—a well received mocking framework for Java projects. We evaluate JMocker on four open source projects and successfully refactored 214 cases in total. The evaluation results show that our framework is efficient, applicable to different projects, and preserves test behaviors. According to the feedback of six real-life developers, JMocker improves the design quality of test cases. JMocker is available at https://github.com/wx930910/JMocker. The tool demo can be found at https://youtu.be/HFoA2ZKCoxM.
{"title":"JMocker","authors":"Xiao Wang, L. Xiao, Tingting Yu, Anne Woepse, Sunny Wong","doi":"10.1145/3510454.3516836","DOIUrl":"https://doi.org/10.1145/3510454.3516836","url":null,"abstract":"Mocking frameworks are dedicated to creating, manipulating, and verifying the execution of \"faked\" objects in unit testing. This helps developers to overcome the challenge of high inter-dependencies among software units. Despite the various benefits offered by existing mocking frameworks, developers often create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. We contribute a refactoring tool as an Eclipse Plugin, named JMocker, to automatically identify and replace the usage of inheritance by using Mockito—a well received mocking framework for Java projects. We evaluate JMocker on four open source projects and successfully refactored 214 cases in total. The evaluation results show that our framework is efficient, applicable to different projects, and preserves test behaviors. According to the feedback of six real-life developers, JMocker improves the design quality of test cases. JMocker is available at https://github.com/wx930910/JMocker. The tool demo can be found at https://youtu.be/HFoA2ZKCoxM.","PeriodicalId":326006,"journal":{"name":"Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116698132","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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