Background: Blogs are a source of grey literature which are widely adopted by software practitioners for disseminating opinion and experience. Analysing such articles can provide useful insights into the state–of–practice for software engineering research. However, there are challenges in identifying higher quality content from the large quantity of articles available. Credibility assessment can help in identifying quality content, though there is a lack of existing corpora. Credibility is typically measured through a series of conceptual criteria, with ’argumentation’ and ’evidence’ being two important criteria. Objective: We create a corpus labelled for argumentation and evidence that can aid the credibility community. The corpus consists of articles from the blog of a single software practitioner and is publicly available. Method: Three annotators label the corpus with a series of conceptual credibility criteria, reaching an agreement of 0.82 (Fleiss’ Kappa). We present preliminary analysis of the corpus by using it to investigate the identification of claim sentences (one of our ten labels). Results: We train four systems (Bert, KNN, Decision Tree and SVM) using three feature sets (Bag of Words, Topic Modelling and InferSent), achieving an F1 score of 0.64 using InferSent and a Linear SVM. Conclusions: Our preliminary results are promising, indicating that the corpus can help future studies in detecting the credibility of grey literature. Future research will investigate the degree to which the sentence level annotations can infer the credibility of the overall document.
背景:博客是灰色文献的来源，被软件从业者广泛采用，用于传播意见和经验。分析这些文章可以为软件工程研究的实践状态提供有用的见解。然而，在从大量可用文章中识别高质量内容方面存在挑战。尽管缺乏现有的语料库，但可信度评估可以帮助识别高质量的内容。可信度通常是通过一系列概念标准来衡量的，其中“论证”和“证据”是两个重要的标准。目的:我们创建一个标记为论证和证据的语料库，可以帮助可信度社区。语料库由来自单个软件从业者博客的文章组成，并且是公开可用的。方法:三位注释者用一系列概念可信度标准对语料库进行标注，一致性为0.82 (Fleiss’Kappa)。我们提出了语料库的初步分析，使用它来调查索赔句(我们的十个标签之一)的识别。结果:我们使用三个特征集(Bag of Words, Topic Modelling和InferSent)训练了四个系统(Bert, KNN, Decision Tree和SVM)，使用InferSent和线性支持向量机获得了0.64的F1分数。结论:我们的初步结果是有希望的，表明语料库可以帮助未来的研究检测灰色文献的可信度。未来的研究将探讨句子级注释在多大程度上可以推断整个文档的可信度。
{"title":"Towards a corpus for credibility assessment in software practitioner blog articles","authors":"Ashley Williams, M. Shardlow, A. Rainer","doi":"10.1145/3463274.3463330","DOIUrl":"https://doi.org/10.1145/3463274.3463330","url":null,"abstract":"Background: Blogs are a source of grey literature which are widely adopted by software practitioners for disseminating opinion and experience. Analysing such articles can provide useful insights into the state–of–practice for software engineering research. However, there are challenges in identifying higher quality content from the large quantity of articles available. Credibility assessment can help in identifying quality content, though there is a lack of existing corpora. Credibility is typically measured through a series of conceptual criteria, with ’argumentation’ and ’evidence’ being two important criteria. Objective: We create a corpus labelled for argumentation and evidence that can aid the credibility community. The corpus consists of articles from the blog of a single software practitioner and is publicly available. Method: Three annotators label the corpus with a series of conceptual credibility criteria, reaching an agreement of 0.82 (Fleiss’ Kappa). We present preliminary analysis of the corpus by using it to investigate the identification of claim sentences (one of our ten labels). Results: We train four systems (Bert, KNN, Decision Tree and SVM) using three feature sets (Bag of Words, Topic Modelling and InferSent), achieving an F1 score of 0.64 using InferSent and a Linear SVM. Conclusions: Our preliminary results are promising, indicating that the corpus can help future studies in detecting the credibility of grey literature. Future research will investigate the degree to which the sentence level annotations can infer the credibility of the overall document.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115506553","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 design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.
{"title":"Recommender Systems for Software Project Managers","authors":"Liang Wei, Luiz Fernando Capretz","doi":"10.1145/3463274.3463951","DOIUrl":"https://doi.org/10.1145/3463274.3463951","url":null,"abstract":"The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125558233","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}
Context: Sustainability has become an important topic for researchers and is gaining popularity among software development companies, but integrating it into their development processes is still lacking. Objectives: This paper aimed to discuss the purpose of doctoral research, the research questions, the steps to answer the research questions, and the research's current progress concerning sustainability in the software development life cycle. Results: I have presented the high-level plans for the doctoral research and outlined the first part of the results of phase 1. As part of this phase 1, I have conducted an extensive literature review to collect data about sustainability in companies' agile methods. I found only a few studies reporting sustainability in agile software development, and this finding proposes that either this field was not studied, or the results have not been widely published, indicating a gap in research.
{"title":"Supporting sustainability design through agile software development","authors":"Hatef Shamshiri","doi":"10.1145/3463274.3463347","DOIUrl":"https://doi.org/10.1145/3463274.3463347","url":null,"abstract":"Context: Sustainability has become an important topic for researchers and is gaining popularity among software development companies, but integrating it into their development processes is still lacking. Objectives: This paper aimed to discuss the purpose of doctoral research, the research questions, the steps to answer the research questions, and the research's current progress concerning sustainability in the software development life cycle. Results: I have presented the high-level plans for the doctoral research and outlined the first part of the results of phase 1. As part of this phase 1, I have conducted an extensive literature review to collect data about sustainability in companies' agile methods. I found only a few studies reporting sustainability in agile software development, and this finding proposes that either this field was not studied, or the results have not been widely published, indicating a gap in research.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131364429","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}
Code completion tools are increasingly important when developing modern software. Recently, statistical language modeling techniques have achieved great success in the code completion task. However, two major issues with these techniques severely affect the performance of neural language models (NLMs) of code completion. a) Long-range dependences are common in program source code. b) New and rare vocabulary in code is much higher than natural language. To address the challenges above, in this paper, we propose code completion with a memory mechanism and a copy mechanism (CCMC). To capture the long-range dependencies in the program source code, we employ Transformer-XL as our base model. To utilize the locally repeated terms in program source code, we apply the pointer network into our base model and design CopyMask to improve the training efficiency, which is inspired by masked multihead attention in the transformer decoder. To combine the long-range dependency modeling ability from Transformer-XL and the ability to copy the input token to output from the pointer network, we design a memory mechanism and a copy mechanism. Through our memory mechanism, our model can uniformly manage the context used by Transformer-XL and pointer network. Through our copy mechanism, our model can either generate a within-vocabulary token or copy an out-of-vocabulary (OOV) token from inputs. Experiments on a real-world dataset demonstrate the effectiveness of our CCMC on the code completion task.
{"title":"CCMC: Code Completion with a Memory Mechanism and a Copy Mechanism","authors":"Hao Yang, Li Kuang","doi":"10.1145/3463274.3463332","DOIUrl":"https://doi.org/10.1145/3463274.3463332","url":null,"abstract":"Code completion tools are increasingly important when developing modern software. Recently, statistical language modeling techniques have achieved great success in the code completion task. However, two major issues with these techniques severely affect the performance of neural language models (NLMs) of code completion. a) Long-range dependences are common in program source code. b) New and rare vocabulary in code is much higher than natural language. To address the challenges above, in this paper, we propose code completion with a memory mechanism and a copy mechanism (CCMC). To capture the long-range dependencies in the program source code, we employ Transformer-XL as our base model. To utilize the locally repeated terms in program source code, we apply the pointer network into our base model and design CopyMask to improve the training efficiency, which is inspired by masked multihead attention in the transformer decoder. To combine the long-range dependency modeling ability from Transformer-XL and the ability to copy the input token to output from the pointer network, we design a memory mechanism and a copy mechanism. Through our memory mechanism, our model can uniformly manage the context used by Transformer-XL and pointer network. Through our copy mechanism, our model can either generate a within-vocabulary token or copy an out-of-vocabulary (OOV) token from inputs. Experiments on a real-world dataset demonstrate the effectiveness of our CCMC on the code completion task.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124828335","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}
Correlative studies carried out by different experts have cited a number of impediments to the growth of cloud computing technology; at the top of the list is security and data privacy issues. More so, a systematic mapping study which was conducted at the start of this research, revealed that the most prevalent cloud security issues are a consequence of poor architecture. 73 percent of literature surveyed also revealed that frameworks and reference architectures are one of the most effective ways of preventing security and data privacy breaches within a cloud computing environment as these issues are addressed during the requirements phase, prior to deployment. [1], [14] This research seeks to explore a preventative approach to cloud security breaches through the use of reference architectures. Firstly, we investigate the main causes of cloud security breaches and then, we analyse existing reference architectures with the aim of designing a universal security framework usable across multi-cloud computing platforms.
{"title":"A Reference Architecture for Validating Security Across Multi-Cloud Computing Systems","authors":"Henry Edet","doi":"10.1145/3463274.3463345","DOIUrl":"https://doi.org/10.1145/3463274.3463345","url":null,"abstract":"Correlative studies carried out by different experts have cited a number of impediments to the growth of cloud computing technology; at the top of the list is security and data privacy issues. More so, a systematic mapping study which was conducted at the start of this research, revealed that the most prevalent cloud security issues are a consequence of poor architecture. 73 percent of literature surveyed also revealed that frameworks and reference architectures are one of the most effective ways of preventing security and data privacy breaches within a cloud computing environment as these issues are addressed during the requirements phase, prior to deployment. [1], [14] This research seeks to explore a preventative approach to cloud security breaches through the use of reference architectures. Firstly, we investigate the main causes of cloud security breaches and then, we analyse existing reference architectures with the aim of designing a universal security framework usable across multi-cloud computing platforms.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125493099","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}
L. Martins, V. Brito, Daniel Feitosa, Larissa Rocha, H. Costa, I. Machado
The teaching-learning process may require specific pedagogical approaches to establish a relationship with industry practices. Recently, some studies investigated the educators’ perspectives and the undergraduate courses curriculum to identify potential weaknesses and solutions for the software testing teaching process. However, it is still unclear how the practitioners evaluate the acquisition of knowledge about software testing in undergraduate courses. This study carried out an expert survey with 68 newly graduated practitioners to determine what the industry expects from them and what they learned in academia. The yielded results indicated that those practitioners learned at a similar rate as others with a long industry experience. Also, they studied less than half of the 35 software testing topics collected in the survey and took industry-backed extracurricular courses to complement their learning. Additionally, our findings point out a set of implications for future research, as the respondents’ learning difficulties (e.g., lack of learning sources) and the gap between academic education and industry expectations (e.g., certifications).
{"title":"From Blackboard to the Office: A Look Into How Practitioners Perceive Software Testing Education","authors":"L. Martins, V. Brito, Daniel Feitosa, Larissa Rocha, H. Costa, I. Machado","doi":"10.1145/3463274.3463338","DOIUrl":"https://doi.org/10.1145/3463274.3463338","url":null,"abstract":"The teaching-learning process may require specific pedagogical approaches to establish a relationship with industry practices. Recently, some studies investigated the educators’ perspectives and the undergraduate courses curriculum to identify potential weaknesses and solutions for the software testing teaching process. However, it is still unclear how the practitioners evaluate the acquisition of knowledge about software testing in undergraduate courses. This study carried out an expert survey with 68 newly graduated practitioners to determine what the industry expects from them and what they learned in academia. The yielded results indicated that those practitioners learned at a similar rate as others with a long industry experience. Also, they studied less than half of the 35 software testing topics collected in the survey and took industry-backed extracurricular courses to complement their learning. Additionally, our findings point out a set of implications for future research, as the respondents’ learning difficulties (e.g., lack of learning sources) and the gap between academic education and industry expectations (e.g., certifications).","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130119744","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}
Jithin Cheriyan, Bastin Tony Roy Savarimuthu, Stephen Cranefield
Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members’ use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms – GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE communities.
{"title":"Towards offensive language detection and reduction in four Software Engineering communities","authors":"Jithin Cheriyan, Bastin Tony Roy Savarimuthu, Stephen Cranefield","doi":"10.1145/3463274.3463805","DOIUrl":"https://doi.org/10.1145/3463274.3463805","url":null,"abstract":"Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members’ use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms – GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE communities.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134159583","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. Sharma, Bastin Tony Roy Savarimuthu, N. Stanger
Governance has been highlighted as a key factor in the success of an Open Source Software (OSS) project. It is generally seen that in a mixed meritocracy and autocracy governance model, the decision-making (DM) responsibility regarding what features are included in the OSS is shared among members from select roles; prominently the project leader. However, less examination has been made whether members from these roles are also prominent in DM discussions and how decisions are made, to show they play an integral role in the success of the project. We believe that to establish their influence, it is necessary to examine not only discussions of proposals in which the project leader makes the decisions, but also those where others make the decisions. Therefore, in this study, we examine the prominence of members performing different roles in: (i) making decisions, (ii) performing certain social roles in DM discussions (e.g., discussion starters), (iii) contributing to the OSS development social network through DM discussions, and (iv) how decisions are made under both scenarios. We examine these aspects in the evolution of the well-known Python project. We carried out a data-driven longitudinal study of their email communication spanning 20 years, comprising about 1.5 million emails. These emails contain decisions for 466 Python Enhancement Proposals (PEPs) that document the language’s evolution. Our findings make the influence of different roles transparent to future (new) members, other stakeholders, and more broadly, to the OSS research community.
{"title":"Influence of Roles in Decision-Making during OSS Development — A Study of Python","authors":"P. Sharma, Bastin Tony Roy Savarimuthu, N. Stanger","doi":"10.1145/3463274.3463326","DOIUrl":"https://doi.org/10.1145/3463274.3463326","url":null,"abstract":"Governance has been highlighted as a key factor in the success of an Open Source Software (OSS) project. It is generally seen that in a mixed meritocracy and autocracy governance model, the decision-making (DM) responsibility regarding what features are included in the OSS is shared among members from select roles; prominently the project leader. However, less examination has been made whether members from these roles are also prominent in DM discussions and how decisions are made, to show they play an integral role in the success of the project. We believe that to establish their influence, it is necessary to examine not only discussions of proposals in which the project leader makes the decisions, but also those where others make the decisions. Therefore, in this study, we examine the prominence of members performing different roles in: (i) making decisions, (ii) performing certain social roles in DM discussions (e.g., discussion starters), (iii) contributing to the OSS development social network through DM discussions, and (iv) how decisions are made under both scenarios. We examine these aspects in the evolution of the well-known Python project. We carried out a data-driven longitudinal study of their email communication spanning 20 years, comprising about 1.5 million emails. These emails contain decisions for 466 Python Enhancement Proposals (PEPs) that document the language’s evolution. Our findings make the influence of different roles transparent to future (new) members, other stakeholders, and more broadly, to the OSS research community.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132768396","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}
Jiahui Geng, Neel Kanwal, M. Jaatun, Chunming Rong
We have entered the era of big data, and it is considered to be the ”fuel” for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals’ privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performance models shared among multiple parties while still complying with user privacy and data confidentiality requirements. Although FL has been intensively studied and used in real applications, there is still limited research related to its prospects and applications as a FLaaS (Federated Learning as a Service) to interested 3rd parties. In this paper, we present a FLaaS system: DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process. We describe particularly the scenario where our DID-eFed enables the FLaaS among hospitals and research institutions.
{"title":"DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities","authors":"Jiahui Geng, Neel Kanwal, M. Jaatun, Chunming Rong","doi":"10.1145/3463274.3463352","DOIUrl":"https://doi.org/10.1145/3463274.3463352","url":null,"abstract":"We have entered the era of big data, and it is considered to be the ”fuel” for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals’ privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performance models shared among multiple parties while still complying with user privacy and data confidentiality requirements. Although FL has been intensively studied and used in real applications, there is still limited research related to its prospects and applications as a FLaaS (Federated Learning as a Service) to interested 3rd parties. In this paper, we present a FLaaS system: DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process. We describe particularly the scenario where our DID-eFed enables the FLaaS among hospitals and research institutions.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129470940","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}
Finding bugs in a commercial cyber-physical system (CPS) development tool such as Simulink is hard as its codebase contains millions of lines of code and complete formal language specifications are not available. While deep learning techniques promise to learn such language specifications from sample models, deep learning needs a large number of training data to work well. SLGPT addresses this problem by using transfer learning to leverage the powerful Generative Pre-trained Transformer 2 (GPT-2) model, which has been pre-trained on a large set of training data. SLGPT adapts GPT-2 to Simulink with both randomly generated models and models mined from open-source repositories. SLGPT produced Simulink models that are both more similar to open-source models than its closest competitor, DeepFuzzSL, and found a super-set of the Simulink development toolchain bugs found by DeepFuzzSL.
{"title":"SLGPT: Using Transfer Learning to Directly Generate Simulink Model Files and Find Bugs in the Simulink Toolchain","authors":"S. L. Shrestha, Christoph Csallner","doi":"10.1145/3463274.3463806","DOIUrl":"https://doi.org/10.1145/3463274.3463806","url":null,"abstract":"Finding bugs in a commercial cyber-physical system (CPS) development tool such as Simulink is hard as its codebase contains millions of lines of code and complete formal language specifications are not available. While deep learning techniques promise to learn such language specifications from sample models, deep learning needs a large number of training data to work well. SLGPT addresses this problem by using transfer learning to leverage the powerful Generative Pre-trained Transformer 2 (GPT-2) model, which has been pre-trained on a large set of training data. SLGPT adapts GPT-2 to Simulink with both randomly generated models and models mined from open-source repositories. SLGPT produced Simulink models that are both more similar to open-source models than its closest competitor, DeepFuzzSL, and found a super-set of the Simulink development toolchain bugs found by DeepFuzzSL.","PeriodicalId":328024,"journal":{"name":"Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116473054","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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