ABSTRACT The video gaming industry has evolved into an industry that drives high revenue and tangentially faces some of the toughest problems in the software engineering domain. With more games being released with either remote multiplayer components or cloud streaming foundations, maintaining an engaging player experience that lives up to a high‐quality bar is a problem that many game studios must overcome. Traditionally, studios have relied on brute‐force manual testing of their software to find impacting bugs. However, as studios and publishers focus on shortening the delivery timeline of their products to consumers, we have seen a rise in the use of testing tools, automation and enhanced processes that focus on achieving a high level of quality. In this systematic literature review, we provide a review of the tools and processes and discuss how these tools enhance the quality experience or help decrease the cost of testing while allowing for faster releases. We conclude by observing some potential areas of opportunity, such as suggesting an approach to help validate the severity of bugs found through community validation and providing a mechanism to create a way to track bugs across heterogeneous devices.

Model-driven software engineering (MDSE) has emerged as a popular and commonly used method for designing software systems in which models are the primary development artifact over the last decade. MDSE has resulted in the trend toward further automating the software process. However, the generation of test cases from user requirements still lags in reaching the required level of automation. Given that most user requirements are written in natural language, the recent advances in natural language processing (NLP) provide an opportunity to further automate the test generation process.In this paper, we exploit the advances in MDSE and NLP to generate abstract test cases from user requirements written in structured natural language and the respective data model. We accomplish this by creating meta-models for user requirements and abstract test cases and defining the appropriate transformation rules. To support this transformation, helper methods are defined to extract the relevant information from user requirements related to testing. To show the feasibility of the approach, we developed a prototype and conducted a case study with use cases and test cases from a Payroll Management System.

The behavior, attributes and properties of a software system is represented in a set of requirements that are written in structured natural language and are usually ambiguous. In large development projects, different modeling techniques are used to create and manage these requirements which aid in the analysis of the problem domain. Requirements are later used in the development process to create test cases, which is still mainly a manual process. To automate this process, we plan to use several of the techniques used in model-driven software development and Natural Language Processing(NLP). The approach under consideration is to use a model-to-model transformation to convert requirements into test cases with the support of Stanford CoreNLP techniques. Key to this transformation process is the use of meta-modeling for requirements and test cases. In this paper we focus on creating a comprehensive meta-model for requirements that can represent both use cases and user stories and performing preliminary analysis of the requirements using NLP. In later work we will develop a set of transformation rules to convert requirements into partial test cases. To show the feasibility of our approach we develop a prototype that can accept a cross-section of requirements written as both use cases and user stories.

Recently there has been an increase in the number of software applications using the microservices architectural pattern. This is due to the benefits derived over the more traditional N-tier patterns that use monolithic designs for each of the tiers. The value of using the microservices architectural pattern, particularly in the cloud, has been pioneered by companies such as Netflix and Google. These companies have created protocols and tools to support the development of cloud-based applications. However, the testing of microservices applications continues to be challenging due to the added complexity of network communication between the collaborating services. In this paper we provide a comparison of several open-source tools used to support the testing of microservices, describe polyglot Ridesharing microservices reference prototype, and present the results of a study that measures the overhead to test the Ridesharing application. We also describe our experiences in configuring and running the testing tools on the prototype.