2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献
Pub Date : 2021-11-24DOI: 10.1109/SNPD51163.2021.9704913
H. Nieto-Chaupis
This paper present a model of software engineering to estimate the social distancing with realistic inputs. This might be incorporated in a smart-phone application in order to get an exact estimate of the values of social distancing in times of global pandemic. Attention is paid on the measurement of outdoor scenarios where wind velocity would play an important role to move the aerosols at distances beyond the known social distances. Thus, the dehydration time emerges also as a predictor of risk to get the infection of virus. The proposed software has capabilities to yield numeric values of risk in terms of probabilities. It is expected that once the associated computational program is running then the permanent assessment of potential scenarios would give concrete values of social distancing. In this manner one expects that these values are uploaded at an Internet network.
{"title":"Software Engineering For Estimation of Social Distancing in Pandemic Times","authors":"H. Nieto-Chaupis","doi":"10.1109/SNPD51163.2021.9704913","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704913","url":null,"abstract":"This paper present a model of software engineering to estimate the social distancing with realistic inputs. This might be incorporated in a smart-phone application in order to get an exact estimate of the values of social distancing in times of global pandemic. Attention is paid on the measurement of outdoor scenarios where wind velocity would play an important role to move the aerosols at distances beyond the known social distances. Thus, the dehydration time emerges also as a predictor of risk to get the infection of virus. The proposed software has capabilities to yield numeric values of risk in terms of probabilities. It is expected that once the associated computational program is running then the permanent assessment of potential scenarios would give concrete values of social distancing. In this manner one expects that these values are uploaded at an Internet network.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121132193","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9705004
Jzau-Sheng Lin, Fang Shen An, Li Cheng Ze
In this paper, we modified a low-cost and rapid method to detect chest X-rays based on MobileNet. Because MobileNet is a lightweight neural network, we modified and optimized backpropagation learning to train the model. In the subsequent COVID-19, pneumonia, and normal tests, the recognition accuracy reached 99.14%, which greatly improved the performance of the model. Our scheme can produce an effective model suitable for low-performance mobile devices.
{"title":"Modified Convolutional Network for the Identification of Covid-19 with a Mobile System","authors":"Jzau-Sheng Lin, Fang Shen An, Li Cheng Ze","doi":"10.1109/SNPD51163.2021.9705004","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9705004","url":null,"abstract":"In this paper, we modified a low-cost and rapid method to detect chest X-rays based on MobileNet. Because MobileNet is a lightweight neural network, we modified and optimized backpropagation learning to train the model. In the subsequent COVID-19, pneumonia, and normal tests, the recognition accuracy reached 99.14%, which greatly improved the performance of the model. Our scheme can produce an effective model suitable for low-performance mobile devices.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129450062","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9704990
Hiroki Nakata, T. Nakanishi
In this paper, we represent an impression extraction method for music by relationship between chords and impression terms. Our methods extract impression term with weights from chord extracted from music as wav file. In addition, we created a module for further application of the music impression extraction method. We designed the system which enable us to retrieve music from the word which has been input by user. We use a data set consisting of a music file and annotation file with an impression terms to each song in order to create models that relates to chord extracted from music with an impression using cosine-similarity. By using this first model, we can retrieve music based on the words that we put. We will realize a recommendation system based on the chords from music and our impression of the word.
{"title":"Music Impression Extraction Method By chord Impressions and Its Application to Music Media Retrieval","authors":"Hiroki Nakata, T. Nakanishi","doi":"10.1109/SNPD51163.2021.9704990","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704990","url":null,"abstract":"In this paper, we represent an impression extraction method for music by relationship between chords and impression terms. Our methods extract impression term with weights from chord extracted from music as wav file. In addition, we created a module for further application of the music impression extraction method. We designed the system which enable us to retrieve music from the word which has been input by user. We use a data set consisting of a music file and annotation file with an impression terms to each song in order to create models that relates to chord extracted from music with an impression using cosine-similarity. By using this first model, we can retrieve music based on the words that we put. We will realize a recommendation system based on the chords from music and our impression of the word.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129479136","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}
EDM (Electrical Discharge Machining) is a process to remove metal from conductive materials using electrical sparks. To monitor the EDM process using virtual metrology (VM), we need to obtain the electrode’s voltage and current signals of a machine tool. Due to the nature of EDM, the sensors installed on the machine tool acquire the signals at a high sampling rate and generate a vast amount of data in a short time, thereby raising the big-data processing issue. Our previous work proposed an efficient approach called BEDPS to process the EDM big data in a Hadoop distributed cluster. This paper presents a novel big data processing approach to feature extraction for EDM by using container technology (i.e., Docker and Kubernetes). We re-implement some Spark algorithms of BEDPS in Python (originally in Scala) and then run the refined BEDPS in containers in a Kubernetes cluster. Testing results show that the refined BEDPS developed in this study can reduce the execution time by almost half, compared to the original Scala version (9.6577 minutes vs. 19.2735 minutes). The adoption of Python in Spark is also shown to have similar performance with Scala, although there are some cases where Python performance falls short, for example, parallel processing using Python parallel processing library. The results also show that the Kubernetes cluster is promising to be an alternative way, other than the Hadoop, for processing big data. At the same time, it can bring some advantages to the big data processing applications, such as easy deployment, robustly running, load balance, self-healing, failover, and horizontal auto-scaling for containerized applications.
{"title":"A Novel Big Data Processing Approach to Feature Extraction for Electrical Discharge Machining based on Container Technology","authors":"Denata Rizky Alimadji, Min-Hsiung Hung, Yu-Chuan Lin, Benny Suryajaya, Chao-Chun Chen","doi":"10.1109/SNPD51163.2021.9704989","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704989","url":null,"abstract":"EDM (Electrical Discharge Machining) is a process to remove metal from conductive materials using electrical sparks. To monitor the EDM process using virtual metrology (VM), we need to obtain the electrode’s voltage and current signals of a machine tool. Due to the nature of EDM, the sensors installed on the machine tool acquire the signals at a high sampling rate and generate a vast amount of data in a short time, thereby raising the big-data processing issue. Our previous work proposed an efficient approach called BEDPS to process the EDM big data in a Hadoop distributed cluster. This paper presents a novel big data processing approach to feature extraction for EDM by using container technology (i.e., Docker and Kubernetes). We re-implement some Spark algorithms of BEDPS in Python (originally in Scala) and then run the refined BEDPS in containers in a Kubernetes cluster. Testing results show that the refined BEDPS developed in this study can reduce the execution time by almost half, compared to the original Scala version (9.6577 minutes vs. 19.2735 minutes). The adoption of Python in Spark is also shown to have similar performance with Scala, although there are some cases where Python performance falls short, for example, parallel processing using Python parallel processing library. The results also show that the Kubernetes cluster is promising to be an alternative way, other than the Hadoop, for processing big data. At the same time, it can bring some advantages to the big data processing applications, such as easy deployment, robustly running, load balance, self-healing, failover, and horizontal auto-scaling for containerized applications.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130217961","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9704994
Y. Chen, L. Hung, M. Syamsudin
In this paper, the reinforcement learning algorithm applied to temperature control of the internet of things (IoT), which aims to develop a multi-purpose intelligent micro-power control switch to achieve advanced temperature control research. This paper is based on the fuzzy Q-learning PID control algorithm based on reinforcement learning, with LinkIt Smart 7688 Duo platform. The error value between the set temperature and the actual sensed temperature is exposed to the reinforcement learning PID control operation. Specifically, a temperature sensor will provide temperature feedback to the LinkIt Smart 7688 Duo in order to achieve the stated temperature control. Finally, the suggested control approach will be compared to PID control to illustrate its efficacy and performance.
{"title":"Fuzzy Q-learning Control for Temperature Systems","authors":"Y. Chen, L. Hung, M. Syamsudin","doi":"10.1109/SNPD51163.2021.9704994","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704994","url":null,"abstract":"In this paper, the reinforcement learning algorithm applied to temperature control of the internet of things (IoT), which aims to develop a multi-purpose intelligent micro-power control switch to achieve advanced temperature control research. This paper is based on the fuzzy Q-learning PID control algorithm based on reinforcement learning, with LinkIt Smart 7688 Duo platform. The error value between the set temperature and the actual sensed temperature is exposed to the reinforcement learning PID control operation. Specifically, a temperature sensor will provide temperature feedback to the LinkIt Smart 7688 Duo in order to achieve the stated temperature control. Finally, the suggested control approach will be compared to PID control to illustrate its efficacy and performance.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122686226","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9704974
S. Biswas, Umesh Singh, Kaustuv Nag
An in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) radio’s self-interference (SI) and co-channel interference (CCI) cancellation strengths usually determine its performance gains over conventional half-duplex ones. Accordingly, this paper explores an alternative to traditional optimization driven design (ODD) techniques available in the literature for beamformer design in IBFD radios. In particular, to mitigate the residual SI and CCI, we propose a run-time data-driven prediction approach to predict the beamforming matrices at the uplink users and the base station. First, we formulate an ODD-based beamforming design problem, which we structurally optimize through sum-rate maximization, and cast it as a second-order cone programming problem. Then, we repeatedly solve this problem to generate a dataset forming a multiple multivariate regression problem. We use the dataset to train a multi-layer perceptron (MLP) employing a supervised learning scheme to solve the associated regression problem. Experimental results demonstrate that the MLP based beamformer design achieves a near-optimal performance at a remarkably high speed for reasonable residual SI and CCI cancellation without the need for explicit channel estimation.
{"title":"Multi-Layer Perceptron-based Beamformer Design for Next-Generation Full-Duplex Cellular Systems","authors":"S. Biswas, Umesh Singh, Kaustuv Nag","doi":"10.1109/SNPD51163.2021.9704974","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704974","url":null,"abstract":"An in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) radio’s self-interference (SI) and co-channel interference (CCI) cancellation strengths usually determine its performance gains over conventional half-duplex ones. Accordingly, this paper explores an alternative to traditional optimization driven design (ODD) techniques available in the literature for beamformer design in IBFD radios. In particular, to mitigate the residual SI and CCI, we propose a run-time data-driven prediction approach to predict the beamforming matrices at the uplink users and the base station. First, we formulate an ODD-based beamforming design problem, which we structurally optimize through sum-rate maximization, and cast it as a second-order cone programming problem. Then, we repeatedly solve this problem to generate a dataset forming a multiple multivariate regression problem. We use the dataset to train a multi-layer perceptron (MLP) employing a supervised learning scheme to solve the associated regression problem. Experimental results demonstrate that the MLP based beamformer design achieves a near-optimal performance at a remarkably high speed for reasonable residual SI and CCI cancellation without the need for explicit channel estimation.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121450987","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9705001
Jhih-Syong Fong, Ya-Hui Chuang, Fu-Sheng Yu, I-Chyn Wey, San-Fu Wang
This paper proposes an Artificial Intelligence (AI) identification algorithm that combined the human body resistance and capacitance sensing. The measured human body impedance data is analyzed by a simple four-arithmetic algorithm, and then four different AI algorithms are used to determine whether or not according to the characteristics of Parkinson’s Disease (PD) patients. The algorithm of this paper is based on the impedance data of normal people and PD patients through the calculation circuit proposed in this paper to analyze the difference in body resistance, the number of finger fits, finger kneading cycles, and finger kneading amplitude to accurately distinguish the fingers of PD patients Symptoms of tremor and stiffness. Through the feature analysis of four AI algorithms, it is judged that the accuracy rate of PD patients is higher than 90%.
{"title":"Wearable Parkinson’s Disease Finger Tapping Quantitative Evaluation Algorithm Combined with Impedance Sensing","authors":"Jhih-Syong Fong, Ya-Hui Chuang, Fu-Sheng Yu, I-Chyn Wey, San-Fu Wang","doi":"10.1109/SNPD51163.2021.9705001","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9705001","url":null,"abstract":"This paper proposes an Artificial Intelligence (AI) identification algorithm that combined the human body resistance and capacitance sensing. The measured human body impedance data is analyzed by a simple four-arithmetic algorithm, and then four different AI algorithms are used to determine whether or not according to the characteristics of Parkinson’s Disease (PD) patients. The algorithm of this paper is based on the impedance data of normal people and PD patients through the calculation circuit proposed in this paper to analyze the difference in body resistance, the number of finger fits, finger kneading cycles, and finger kneading amplitude to accurately distinguish the fingers of PD patients Symptoms of tremor and stiffness. Through the feature analysis of four AI algorithms, it is judged that the accuracy rate of PD patients is higher than 90%.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106027","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9704923
Pei-Chun Lai, Jau-Ji Shen, Yung-Chen Chou
The security of transmitting data over the Internet is always of great concern. As a popular research topic, an efficient steganographic technique aims to hide a secret message for secure transmission over the Internet. In this paper, we propose a new information hiding method based on the difference value between the maximum and minimum pixels in an image block and using Modified Least Significant Bit (LSB) substitution strategy to conceal data. The difference value will be in one of three levels (lower, middle, and higher). Using Modified LSB substitution, the lower, middle, and higher levels correspond to 3-, 4-, and 5-bit embedded secret data, respectively. The experimental results demonstrate that the embedding capacity of the proposed method is greater than previous contributions and maintain a good image quality of stego images that are generated by the proposed method.
{"title":"A High Embedding Capacity Steganographic Method Using Maximum and Minimum pixels Difference Adaptive Strategy","authors":"Pei-Chun Lai, Jau-Ji Shen, Yung-Chen Chou","doi":"10.1109/SNPD51163.2021.9704923","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704923","url":null,"abstract":"The security of transmitting data over the Internet is always of great concern. As a popular research topic, an efficient steganographic technique aims to hide a secret message for secure transmission over the Internet. In this paper, we propose a new information hiding method based on the difference value between the maximum and minimum pixels in an image block and using Modified Least Significant Bit (LSB) substitution strategy to conceal data. The difference value will be in one of three levels (lower, middle, and higher). Using Modified LSB substitution, the lower, middle, and higher levels correspond to 3-, 4-, and 5-bit embedded secret data, respectively. The experimental results demonstrate that the embedding capacity of the proposed method is greater than previous contributions and maintain a good image quality of stego images that are generated by the proposed method.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128184994","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9704983
Takumi Kanehira, Akito Monden, Zeynep Yücel
The correlation coefficient is commonly used in analyses of software project data sets for the purpose of quantifying the relationship between two variables. However, while there are various types of relationships between two variables, the correlation coefficient cannot distinguish between these types. This study proposes new metrics between two continuous variables that have the potential to characterize the relationship types.
{"title":"Association Metrics Between Two Continuous Variables for Software Project Data","authors":"Takumi Kanehira, Akito Monden, Zeynep Yücel","doi":"10.1109/SNPD51163.2021.9704983","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704983","url":null,"abstract":"The correlation coefficient is commonly used in analyses of software project data sets for the purpose of quantifying the relationship between two variables. However, while there are various types of relationships between two variables, the correlation coefficient cannot distinguish between these types. This study proposes new metrics between two continuous variables that have the potential to characterize the relationship types.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116422192","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 : 2021-11-24DOI: 10.1109/SNPD51163.2021.9704927
Hiroto Nakahara, Akito Monden, Zeynep Yücel
Software quality assurance (SQA) is a series of activities within the software development lifecycle that repetitively verify or test the software deliverables to ensure their quality. In this paper, we propose a simulation model of SQA to quantitatively demonstrate the positive effect of adding quality assurance (QA) effort especially in early phases of software development. The proposed model can represent the relationship among the number of bugs in each phase, the amount of QA effort, the expected number of detectable bugs and the amount of bug fixing effort. The model can simulate the different QA strategies in a given software development context; thus, it is useful to identify the best or better strategies to improve software quality with smaller QA and bug fixing effort.
{"title":"A Simulation Model of Software Quality Assurance in the Software Lifecycle","authors":"Hiroto Nakahara, Akito Monden, Zeynep Yücel","doi":"10.1109/SNPD51163.2021.9704927","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704927","url":null,"abstract":"Software quality assurance (SQA) is a series of activities within the software development lifecycle that repetitively verify or test the software deliverables to ensure their quality. In this paper, we propose a simulation model of SQA to quantitatively demonstrate the positive effect of adding quality assurance (QA) effort especially in early phases of software development. The proposed model can represent the relationship among the number of bugs in each phase, the amount of QA effort, the expected number of detectable bugs and the amount of bug fixing effort. The model can simulate the different QA strategies in a given software development context; thus, it is useful to identify the best or better strategies to improve software quality with smaller QA and bug fixing effort.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124775210","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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