Pub Date : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10180973
Rizal Broer Bahaweres, Aldi Zulfikar, I. Hermadi, A. Suroso, Y. Arkeman
Software defects are common when it comes to software development. However, in reality, this is very detrimental for companies and organizations that are developing software. Prediction of software defects in the early stages of development can be a solution to this problem. Of course, the method used needs to be considered when developing a model for predicting software defects. The software continues to experience development, so the prediction model must always be updated so that it can adapt to existing conditions. This study proposes the MLOps approach, which combines development and operation processes to develop a software defect prediction model. We will create a prediction model and then create a Docker and Kubernetes pipeline to automate the entire software defect prediction process so that it can speed up the development process and have good performance. We are comparing the performance evaluation results of the proposed method with the traditional method, which is run manually by Docker. The results showed that the entire source dataset had a fairly good accuracy rate of 76%-83% and a good recall rate of 79%-94%. The precision and recall values were also very good. Apart from that, it also produces a good Fl-score value of 84%-90%. And the development time until the model’s release is shorter: the average time is 7:02 minutes. Performance monitoring on the built-in web server is also easy to do and shows very good results. The web server can receive up to 156. $6/$sec requests in all models based on the dataset used, with the highest error rate at 45.03%. The use of the Docker and Kubernetes pipelines with the MLOps approach has been proven to have good performance, and the development of software defect models can be sped up.
{"title":"Docker and Kubernetes Pipeline for DevOps Software Defect Prediction with MLOps Approach","authors":"Rizal Broer Bahaweres, Aldi Zulfikar, I. Hermadi, A. Suroso, Y. Arkeman","doi":"10.1109/ISMODE56940.2022.10180973","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10180973","url":null,"abstract":"Software defects are common when it comes to software development. However, in reality, this is very detrimental for companies and organizations that are developing software. Prediction of software defects in the early stages of development can be a solution to this problem. Of course, the method used needs to be considered when developing a model for predicting software defects. The software continues to experience development, so the prediction model must always be updated so that it can adapt to existing conditions. This study proposes the MLOps approach, which combines development and operation processes to develop a software defect prediction model. We will create a prediction model and then create a Docker and Kubernetes pipeline to automate the entire software defect prediction process so that it can speed up the development process and have good performance. We are comparing the performance evaluation results of the proposed method with the traditional method, which is run manually by Docker. The results showed that the entire source dataset had a fairly good accuracy rate of 76%-83% and a good recall rate of 79%-94%. The precision and recall values were also very good. Apart from that, it also produces a good Fl-score value of 84%-90%. And the development time until the model’s release is shorter: the average time is 7:02 minutes. Performance monitoring on the built-in web server is also easy to do and shows very good results. The web server can receive up to 156. $6/$sec requests in all models based on the dataset used, with the highest error rate at 45.03%. The use of the Docker and Kubernetes pipelines with the MLOps approach has been proven to have good performance, and the development of software defect models can be sped up.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121346272","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 : 2022-12-22DOI: 10.1109/ISMODE56940.2022.10181006
O. Heriana, A. B. Suksmono, H. Zakaria, A. Prahasta
Light field images have much more information than regular images. One of the advantages is that this processing can produce depth map images. However, there is a problem, the resulting depth map image is inconsistent due to the influence of lighting variations around the object. Therefore it is necessary to make adjustments so that the depth map value matches the actual size. The method proposed in this paper is to adjust the depth map image with the slit lamp guide curve. The object being measured is a ground truth reference object with some defined surface height used for measurement and calibration, and the other object is an eyeball phantom. First, a consumer light field camera is attached to a slit lamp for image acquisition. In the next step, the depth map obtained from the light field image is adjusted to increase the range of pixel values. The third step is the depth calculation of the projected slit light. The last step is the adjustment of the object surface curve based on the depth obtained from the projected slit light. Based on testing on both objects, the proposed method can improve the curvature of the depth map.
{"title":"Curve-based Depth Map Image Adjustment for Eyeball Phantom Measurement","authors":"O. Heriana, A. B. Suksmono, H. Zakaria, A. Prahasta","doi":"10.1109/ISMODE56940.2022.10181006","DOIUrl":"https://doi.org/10.1109/ISMODE56940.2022.10181006","url":null,"abstract":"Light field images have much more information than regular images. One of the advantages is that this processing can produce depth map images. However, there is a problem, the resulting depth map image is inconsistent due to the influence of lighting variations around the object. Therefore it is necessary to make adjustments so that the depth map value matches the actual size. The method proposed in this paper is to adjust the depth map image with the slit lamp guide curve. The object being measured is a ground truth reference object with some defined surface height used for measurement and calibration, and the other object is an eyeball phantom. First, a consumer light field camera is attached to a slit lamp for image acquisition. In the next step, the depth map obtained from the light field image is adjusted to increase the range of pixel values. The third step is the depth calculation of the projected slit light. The last step is the adjustment of the object surface curve based on the depth obtained from the projected slit light. Based on testing on both objects, the proposed method can improve the curvature of the depth map.","PeriodicalId":335247,"journal":{"name":"2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124990249","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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