Improving the automated testing of computer games

电脑游戏是一种快速增长的应用程序类型。预计2020年游戏行业的收入将达到近1600亿美元，远远超过电影业等其他受欢迎的娱乐行业。由于游戏行业的规模，以及游戏玩家极难取悦的事实，开发一款成功的游戏变得具有挑战性，而且往往成本高昂。由于游戏玩家的挑剔和游戏开发商之间的激烈竞争，在发布游戏之前对其进行详尽的测试是必不可少的。然而，由于现代游戏往往很复杂，并且有许多可能的游戏内事件序列(即与主要角色(S)的交互)，因此为游戏创建详尽的测试套件并不是一件琐碎和耗时的事情。理想情况下，创建和执行游戏测试套件的过程可以自动化。不幸的是，游戏的自动化测试是极其困难的，而且在实践中并不经常完成。在这个项目中，我们建议通过利用数据来帮助更好地集中自动测试生成来提高计算机游戏自动化测试的最新水平。我们的目标是(1)了解当前的工业游戏测试实践，(2)采用自动游戏的方法来支持自动测试生成，(3)对需要测试预言(即预期的测试结果)的生成的测试用例进行优先排序，以及(4)对需要由质量保证团队处理的失败测试用例进行优先排序。

Computer games are a rapidly growing application genre. With a projected revenue of almost $160 billion in 2020, the gaming industry has significantly outgrown other popular entertainment industries, such as the movie industry. Because of the scale of the gaming industry, and the fact that gamers are extremely hard to please, it has become challenging, and often costly, to develop a successful game. Because of the pickiness of gamers, and the intense competition between game developers, it is essential to exhaustively test a game before its release. However, as modern games tend to be complex and have many possible in-game event sequences (i.e., interactions with the main character(s)), creating an exhaustive test suite for a game is not trivial and time consuming. Ideally, the process of creating and executing game test suites can be automated. Unfortunately, automated testing of games is extremely difficult and not often done in practice.In this project, we propose to improve the state-of-the-art of automated testing of computer games by leveraging data to help better focus the automated test generation. Our goals are to (1) understand current industrial game testing practices, (2) adapt approaches for automated gameplay to support automated test generation, (3) prioritize generated test cases that need test oracles (i.e., expected test outcomes) and (4) prioritize failing test cases that need to be dealt with by the quality assurance team.