Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00023
Yixin Fan, Guozhi Hao, Jun Wu
Currently, Machine Learning as a Service (MLaaS) greatly benefits artificial intelligence (AI) model trading. However, threats such as model piracy and patent grabbing devastatingly violate the copyright of AI models. Current invasive copyright protection solutions mainly rely on watermarking to embed specific information into AI models, which inevitably decreases the accuracy. While non-invasive schemes, such as adversarial samples, cannot guarantee uniqueness as the adversarial sample generation algorithm would be known to all traders, and thus need to be changed after trading. To enable the ownership information transferable across AI model trading, we propose a blockchain-driven Non-Fungible Token (NFT) approach for trading-oriented AI model copyright protection. We design a mapping mechanism from AI models parameters to NFTs which can identify uniqueness and ownership of AI models across trading. Besides, a reputation-based rewards and penalties scheme is proposed to prevent NFT piracy. Lastly, the evaluation verifies the applicability of our approach.
{"title":"Transferable Unique Copyright Across AI Model Trading: A Blockchain-Driven Non-Fungible Token Approach","authors":"Yixin Fan, Guozhi Hao, Jun Wu","doi":"10.1109/QRS-C57518.2022.00023","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00023","url":null,"abstract":"Currently, Machine Learning as a Service (MLaaS) greatly benefits artificial intelligence (AI) model trading. However, threats such as model piracy and patent grabbing devastatingly violate the copyright of AI models. Current invasive copyright protection solutions mainly rely on watermarking to embed specific information into AI models, which inevitably decreases the accuracy. While non-invasive schemes, such as adversarial samples, cannot guarantee uniqueness as the adversarial sample generation algorithm would be known to all traders, and thus need to be changed after trading. To enable the ownership information transferable across AI model trading, we propose a blockchain-driven Non-Fungible Token (NFT) approach for trading-oriented AI model copyright protection. We design a mapping mechanism from AI models parameters to NFTs which can identify uniqueness and ownership of AI models across trading. Besides, a reputation-based rewards and penalties scheme is proposed to prevent NFT piracy. Lastly, the evaluation verifies the applicability of our approach.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124384534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00034
Chenxi He, Wenhong Liu, Shuang Zhao, Jiawei Liu, Yang Yang
Due to the rapid development of deep neural networks, in recent years, machine translation software has been widely adopted in people's daily lives, such as communicating with foreigners or understanding political news from the neighbouring countries, and it is embedded in daily applications such as Twitter and WeChat. The neural machine translation (NMT) model is the core of machine translation software, and it is very challenging to test it as a deep neural network model due to the Inexplicability of neural networks and the complexity of model output. In this paper, we introduce three latest machine translation testing methods and provide a preliminary analysis of their effects.
{"title":"An Empirical Study towards Characterizing Neural Machine Translation Testing Methods","authors":"Chenxi He, Wenhong Liu, Shuang Zhao, Jiawei Liu, Yang Yang","doi":"10.1109/QRS-C57518.2022.00034","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00034","url":null,"abstract":"Due to the rapid development of deep neural networks, in recent years, machine translation software has been widely adopted in people's daily lives, such as communicating with foreigners or understanding political news from the neighbouring countries, and it is embedded in daily applications such as Twitter and WeChat. The neural machine translation (NMT) model is the core of machine translation software, and it is very challenging to test it as a deep neural network model due to the Inexplicability of neural networks and the complexity of model output. In this paper, we introduce three latest machine translation testing methods and provide a preliminary analysis of their effects.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129192280","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 prediction of Quality of Service (QoS) significantly facilitates the web services selection for QoS based web service recommender systems. One effective method for predicting web services' QoS values is the collaborative filtering (CF) algorithm. However, the existing CF algorithms experience potential scalability issues, as well as the accuracy issues. We present a load- and location-aware collaborative filtering algorithm (LLCF) to improve the prediction accuracy and the scalability. To assess the proposed LLCF, we leverage Amazon Cloud platform where hosts various web services. The experiments are conducted based on selected web services where QoS values are collected. The results show the prediction accuracy is significantly improved by the proposed LLCF. Furthermore, complexity analysis results show that our LLCF can remarkably improve the scalability.
{"title":"LLCF: A Load- and Location-Aware Collaborative Filtering Algorithm to Predict QoS of Web Service","authors":"Chen Li, Xiaochun Zhang, Chengyuan Yu, Xin Shu, Xiaopeng Xu","doi":"10.1109/QRS-C57518.2022.00111","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00111","url":null,"abstract":"The prediction of Quality of Service (QoS) significantly facilitates the web services selection for QoS based web service recommender systems. One effective method for predicting web services' QoS values is the collaborative filtering (CF) algorithm. However, the existing CF algorithms experience potential scalability issues, as well as the accuracy issues. We present a load- and location-aware collaborative filtering algorithm (LLCF) to improve the prediction accuracy and the scalability. To assess the proposed LLCF, we leverage Amazon Cloud platform where hosts various web services. The experiments are conducted based on selected web services where QoS values are collected. The results show the prediction accuracy is significantly improved by the proposed LLCF. Furthermore, complexity analysis results show that our LLCF can remarkably improve the scalability.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128177750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00052
Wei Guo, Leyang Zhou, Jia Liu, Miaomiao Liu
Based on a systematical discussion of the logical relationship between social mentality as a psychological basis of social actions and institutions and social governance, and the online emotion as the core element of the dynamic tendency of internet-based social mentality to form emotional energy to promote the operation of the internet society, this paper conducts an empirical study on the online social mentality and public sentiment guidance during the COVID-19 epidemic in mainland China. We use more than 1 million Weibo dynamic data of 104 accounts of three different types including official media, self-media, and big V media and conduct emotional calculation and judgment to address our objectives. The results show that the public sentiment presented by Weibo as the carrier is mainly positive, among which the official media play a positive role in guiding emotions, while the role played by big Vs' is limited during the COVID-19 epidemic. There exists different public sentiment stemmed from the regional differences brought by the heterogeneity of social governance, economic and social development beyond the media guidance. The study provides valuable internet governance experience on how the government can guide the public to respond to and deal with the crisis with a positive attitude when major public health emergencies occur in the future.
{"title":"Internet Governance: Social Mentality and Public Emotion Analysis on Online Media during the COVID-19 Epidemic in Mainland China","authors":"Wei Guo, Leyang Zhou, Jia Liu, Miaomiao Liu","doi":"10.1109/QRS-C57518.2022.00052","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00052","url":null,"abstract":"Based on a systematical discussion of the logical relationship between social mentality as a psychological basis of social actions and institutions and social governance, and the online emotion as the core element of the dynamic tendency of internet-based social mentality to form emotional energy to promote the operation of the internet society, this paper conducts an empirical study on the online social mentality and public sentiment guidance during the COVID-19 epidemic in mainland China. We use more than 1 million Weibo dynamic data of 104 accounts of three different types including official media, self-media, and big V media and conduct emotional calculation and judgment to address our objectives. The results show that the public sentiment presented by Weibo as the carrier is mainly positive, among which the official media play a positive role in guiding emotions, while the role played by big Vs' is limited during the COVID-19 epidemic. There exists different public sentiment stemmed from the regional differences brought by the heterogeneity of social governance, economic and social development beyond the media guidance. The study provides valuable internet governance experience on how the government can guide the public to respond to and deal with the crisis with a positive attitude when major public health emergencies occur in the future.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132103164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00085
Huan Jin, Hongyan Wan, Ziruo Li, Wenxuan Wang
User comments are one of the main ways for IT companies to obtain software evolution requirements. There are two major methods used to classify the software requirements: the traditional user requirements mining method and the user comments requirements mining. The advantage of traditional user requirements mining is that it can communicate with users face to face, but it is time consuming and the results may not be accurate. Therefore, in this paper, we use the user comments requirements mining method to compare the labeling effect of classification methods on the data set of 19,673 comments. The experimental results show that the combination of TF-IDF and logistic regression (LR) works best on the labeled dataset. This experiment combined with word cloud map has excellent effect on obtaining user requirements.
{"title":"An Empirical Study on Software Requirements Classification Method based on Mobile App User Comments","authors":"Huan Jin, Hongyan Wan, Ziruo Li, Wenxuan Wang","doi":"10.1109/QRS-C57518.2022.00085","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00085","url":null,"abstract":"User comments are one of the main ways for IT companies to obtain software evolution requirements. There are two major methods used to classify the software requirements: the traditional user requirements mining method and the user comments requirements mining. The advantage of traditional user requirements mining is that it can communicate with users face to face, but it is time consuming and the results may not be accurate. Therefore, in this paper, we use the user comments requirements mining method to compare the labeling effect of classification methods on the data set of 19,673 comments. The experimental results show that the combination of TF-IDF and logistic regression (LR) works best on the labeled dataset. This experiment combined with word cloud map has excellent effect on obtaining user requirements.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131808037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00103
Haocheng Guan, Dongcheng Li, Hui Li, Man Zhao
Cross-site scripting attacks, as a means of attack against Web applications, are widely used in phishing, information theft and other fields by unscrupulous people because of their wide targeting and hidden implementation methods. Nevertheless, cross-site scripting vulnerability detection is still in its infancy, with plenty of challenges not yet fully explored. In this paper, we propose Crawler-based Cross Site Scripting Detector, a tool based on crawler technology that can effectively detect stored Cross Site Scripting vulnerabilities and reflected Cross Site Scripting vulnerabilities. Subsequently, in order to verify the effectiveness of the tool, we experim ented this tool with existing tools such as XSSer and Burp Suite by selecting 100 vulnerable websites for the tool's efficiency, false alarm rate and underreporting rate. The results show that our tool can effectively detect Cross Site Scripting vulnerabilities.
{"title":"A Crawler-Based Vulnerability Detection Method for Cross-Site Scripting Attacks","authors":"Haocheng Guan, Dongcheng Li, Hui Li, Man Zhao","doi":"10.1109/QRS-C57518.2022.00103","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00103","url":null,"abstract":"Cross-site scripting attacks, as a means of attack against Web applications, are widely used in phishing, information theft and other fields by unscrupulous people because of their wide targeting and hidden implementation methods. Nevertheless, cross-site scripting vulnerability detection is still in its infancy, with plenty of challenges not yet fully explored. In this paper, we propose Crawler-based Cross Site Scripting Detector, a tool based on crawler technology that can effectively detect stored Cross Site Scripting vulnerabilities and reflected Cross Site Scripting vulnerabilities. Subsequently, in order to verify the effectiveness of the tool, we experim ented this tool with existing tools such as XSSer and Burp Suite by selecting 100 vulnerable websites for the tool's efficiency, false alarm rate and underreporting rate. The results show that our tool can effectively detect Cross Site Scripting vulnerabilities.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132167090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00117
Chuan Zhao, Huilei Cao
We consider a destination port logistics service provider (DPLSP), which wants to improve its service quality by reducing risk of delivery time delay. This paper diagnoses potential risk factors that estimate the performances of the DPLSP who provides services only after the arrival of freight, with the intention of reducing supply chain risk and improve supply chain performance through creative computing approach. Self-organizing feature map (SOFM) computing is a type of artificial neural network based on an unsupervised learning algorithm. We propose the approach of SOFM computing for the purpose of clustering risk data of DPLSPs from a less subjective perspective and then rank the cluster results into different levels based on the total risk value of each cluster. Numerical studies to test the effectiveness of this model would be carried out using air import logistics lead-time reports from a large DPLSP. The results illustrate that the proposed approach could successfully cluster and rank the risk data according to their values.
{"title":"Risk Evaluation of the Destination Port Logistics based on Self-Organizing Map Computing","authors":"Chuan Zhao, Huilei Cao","doi":"10.1109/QRS-C57518.2022.00117","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00117","url":null,"abstract":"We consider a destination port logistics service provider (DPLSP), which wants to improve its service quality by reducing risk of delivery time delay. This paper diagnoses potential risk factors that estimate the performances of the DPLSP who provides services only after the arrival of freight, with the intention of reducing supply chain risk and improve supply chain performance through creative computing approach. Self-organizing feature map (SOFM) computing is a type of artificial neural network based on an unsupervised learning algorithm. We propose the approach of SOFM computing for the purpose of clustering risk data of DPLSPs from a less subjective perspective and then rank the cluster results into different levels based on the total risk value of each cluster. Numerical studies to test the effectiveness of this model would be carried out using air import logistics lead-time reports from a large DPLSP. The results illustrate that the proposed approach could successfully cluster and rank the risk data according to their values.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129265858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00112
Lingfei Ma, Liming Nie, Chenxi Mao, Yaowen Zheng, Y. Liu
In this paper, we propose an analytical model that can analyze the impact of emergencies on open source software (OSS) development. As the core of this model, a metric system is used to comprehensively describe the OSS development process, which includes three dimensions: team activity, development activity, and development risk, with a total of 30 metrics. To demonstrate the effectiveness of the model, we construct an empirical study analyzing the impact of COVID-19 on OSS development. This study is based on the development process events between January 2019 and April 2022 belonging to 50 selected open source projects on GitHub. The results show that more than 72.4% of projects were negatively impacted following the COVID-19 outbreak. Interestingly, we observe that variants of covide-19 did not exacerbate its impact on software development. On the contrary, some project development activities have obviously resumed, indicating that the development team has adapted and gradually got rid of the impact of the epidemic.
{"title":"An Empirical Study of the Impact of COVID-19 on OSS Development","authors":"Lingfei Ma, Liming Nie, Chenxi Mao, Yaowen Zheng, Y. Liu","doi":"10.1109/QRS-C57518.2022.00112","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00112","url":null,"abstract":"In this paper, we propose an analytical model that can analyze the impact of emergencies on open source software (OSS) development. As the core of this model, a metric system is used to comprehensively describe the OSS development process, which includes three dimensions: team activity, development activity, and development risk, with a total of 30 metrics. To demonstrate the effectiveness of the model, we construct an empirical study analyzing the impact of COVID-19 on OSS development. This study is based on the development process events between January 2019 and April 2022 belonging to 50 selected open source projects on GitHub. The results show that more than 72.4% of projects were negatively impacted following the COVID-19 outbreak. Interestingly, we observe that variants of covide-19 did not exacerbate its impact on software development. On the contrary, some project development activities have obviously resumed, indicating that the development team has adapted and gradually got rid of the impact of the epidemic.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00092
M. Tuo, Xiaoqiang Zhao, Bo Shen, Wen-Ling Wu
In this paper, a formal method for real-time verification of Cyber-Physical Systems(CPS) is proposed. Firstly, the CPS behavior model is established by using temporal automata, and then the real-time verification of the system is performed. Based on this method, the real-time task scheduling of the control software of an intelligent car is analyzed. The experimental results show the effectiveness of this method.
{"title":"Modeling and Real-Time Verification for CPS based on Time Automata","authors":"M. Tuo, Xiaoqiang Zhao, Bo Shen, Wen-Ling Wu","doi":"10.1109/QRS-C57518.2022.00092","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00092","url":null,"abstract":"In this paper, a formal method for real-time verification of Cyber-Physical Systems(CPS) is proposed. Firstly, the CPS behavior model is established by using temporal automata, and then the real-time verification of the system is performed. Based on this method, the real-time task scheduling of the control software of an intelligent car is analyzed. The experimental results show the effectiveness of this method.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116966530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00078
Xiao Chen, Junhua Wu
Developers tend to search and reuse code snippets from large-scale corpora while implementing some of the features that existed in development. This will improve the efficiency of development. Code search is to search for semantically relevant code snippets based on a given natural language query. In existing methods, the semantic similarity between code and query is quantified as their distance in the shared vector space. To improve the vector space and map the code vector and query vector into a shared vector space so that the semantically similar code-query pairs are close to each other, we propose a code search method with multimodal representations. It can better enhance the semantic relationship between code snippets and queries. Experiments on Java datasets show that the multimodal representation model MulCS improves the quality of code search. MulCS outperforms several existing advanced models in several performance metrics.
{"title":"Code Search Method Based on Multimodal Representation","authors":"Xiao Chen, Junhua Wu","doi":"10.1109/QRS-C57518.2022.00078","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00078","url":null,"abstract":"Developers tend to search and reuse code snippets from large-scale corpora while implementing some of the features that existed in development. This will improve the efficiency of development. Code search is to search for semantically relevant code snippets based on a given natural language query. In existing methods, the semantic similarity between code and query is quantified as their distance in the shared vector space. To improve the vector space and map the code vector and query vector into a shared vector space so that the semantically similar code-query pairs are close to each other, we propose a code search method with multimodal representations. It can better enhance the semantic relationship between code snippets and queries. Experiments on Java datasets show that the multimodal representation model MulCS improves the quality of code search. MulCS outperforms several existing advanced models in several performance metrics.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114565754","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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