# Classification of Bugs in Cloud Computing Applications Using Machine Learning Techniques
## 1 Introduction  
Software as a Service (SaaS) is the concept of hosting ready-to-use applications for customers, which customers can access online. SaaS is among the main categories in cloud computing, including Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). In SaaS, multiple customers can access the same copy of the application that the service provider creates. Depending on the Service Level Agreement (SLA) between the service providers and service users, the service usage can be charged accordingly. Agreements are also per the usage of the services or can be monthly or yearly. SaaS applications are currently deployed in many industries such as emails, financial applications, and human resources applications [1].  
In SaaS models, users have full access to use applications as per their agreement. The service providers develop, deploy, and maintain applications, and users can use those applications to achieve their tasks. SaaS applications can be accessed through web browsers. Application users do not have to worry about installations and the applications'maintenance, as the applications vendor is responsible for this process. In some cases, users have to download plugins to perform different functionalities in the application [2].  
The usage of SaaS applications in the cloud is increasing daily, and these applications are also expanding. With the increase in usage, application bugs can result in poor user experience. Maintaining these applications is also a challenging task that is time-consuming and impacts cost. To make the development and maintenance of these applications easy, there is a need to investigate the bugs occurring in these applications so that users can classify and prioritize these defects. Based on these priorities, developers can quickly proceed with the fixation process. The manual process of classifying or setting the bugs' priority is time-consuming. Thus, there is a need to automate the activity of bug classification and prioritization [3].  
The motivation for classifying bugs in cloud computing applications using machine learning techniques stems from the increasing importance and widespread adoption of cloud computing in today's world. Cloud computing has become an essential tool for businesses and organizations of all sizes, offering a range of benefits such as increased efficiency, scalability, and cost savings. However, as the number of cloud computing applications continues to grow, the number of bugs that arise in these systems do too. Bugs can cause significant downtime and negatively impact the performance of cloud computing systems, leading to lost productivity, decreased customer satisfaction, and potential financial losses. As such, there is a growing need to develop effective methods for detecting and resolving bugs in cloud computing applications. Machine learning algorithms have shown great promise in addressing this problem, as they can process large amounts of data and detect patterns indicative of bugs. The classification of bugs in cloud computing applications using machine learning techniques is motivated by improving bug detection, resolution efficiency, and accuracy. This approach can help organizations to quickly identify the source of bugs, prioritize bug resolution efforts, and prevent downtime and other negative impacts on cloud computing systems. The use of machine learning techniques for bug classification can also help to reduce the workload of developers and system administrators, allowing them to focus on more strategic tasks and further improve the overall efficiency and reliability of cloud computing systems. In conclusion, the classification of bugs in cloud computing applications using machine learning techniques represents a crucial area of research and development. It can greatly improve cloud computing systems' stability, reliability, and performance and, ultimately, benefit businesses and organizations of all sizes.