How Mozilla Tests Firefox With A Machine Learning Model
The Continuous Integration system at Mozilla includes 85,000 files, each containing many test functions. These tests need to run on all platforms – Ios, Windows, and Linux. However, it is impossible to test each function on all platforms. Instead, Mozilla had developed a strategy to test 90 unique functions, since running every configuration would amount to 2.3 billion test files per day. The functions were chosen through the principles of importance and relevancy, and the configurations were tested through the integration branch. However, the heuristics of ranking tests based on the frequency of failure was naive as it did not consider the contents of the patch. Additionally, choosing tests can be a time-consuming task and may lead to over selection. The Mozilla developers chose to run machine learning algorithms as they hypothesised that it could result in the quicker, efficient, and economical selection of optimal tests to run. Hence, the developers constructed infrastructure to ensure the smooth execution of the CI pipeline. Getting Started To build the training model, the developers initially had to solve the problem of naive heuristics. They built a set of complex heuristics to predict which patch causes which regression. Some failures are classified/annotated by humans as ‘intermittent’ or ‘fixed by commit’ and help find the regressions of the missing or intermittent tests. Since 100% accuracy is not attainable, the developers have built heuristics to evaluate the classifications. Apart from solving the problem of the heuristic, the developers also have to collect data on the patches themselves. They correlate with the test failure data, which provides deterministic results for ML models as to which tests are more likely to fail for a given patch. With the complex heuristics, dataset of patches and associated tests, the developers at Mozilla have built a training set and a validation set to teach the ML model how to select optimal tests. 90% of the data set is the training set, and the other 10% is the validation set. The validation is carefully chosen to be posterior to the training set to avoid information leakage. The preventative measure reduces the risk of a biased and artificially enhanced ML model. Mozilla developers then train XGBoost models using tests, patches, and links features. The model is trained using the TUPLE test patch input, and the output is a single binary that determines whether or not the patch passes the test. A single model can run all tests. Optimizing Configuration To further optimise the running of tests, the developers choose to test and improve the selection of the tests and where the tests should run. By doing so, they can identify the redundant configurations with the help of the datasets that have been collected. These redundancies are identified; the developers use solutions similar to frequent itemset mining. Results The model provides frontend and backend services, and RedisQueues bridges the gap. The frontend exposes a simple REST API, so consumers need to only specify the push they are interested in (identified by the branch and topmost revision). The backend will automatically determine the files changed and their contents using a clone of Mozilla-central. Depending on the size of the push, the service can run up to a few minutes to yield the desired results. Developers only queue one job at a time and cache the results once computed. Developers at Mozilla are using the model for scheduling tasks on the integration branch and running the special mach try auto command to test changes on the testing branch. Performance Assessment Finally, the developers assess, measure and compare the results and success of the algorithms. The variables kept in mind are- (i) the number of resources used (Hours, dollars), and (ii) the regression detection rate. Alongside the scheduled algorithm, a shadow scheduler showcases the output it would have scheduled if it was the default. The scheduler effectiveness of the various shadows is plotted on a dashboard, and the algorithm with the best performance is made the default algorithm. Source: https://hacks.mozilla.org/2020/12/cross-browser-testing-part-1-web-app-testing-today/
Explanation on how Mozilla tests Firefox with the help of a machine learning model infrastructure
Explanation on how Mozilla tests Firefox with the help of a machine learning model infrastructure which involves regression dataset
["Mozilla"]
["Go", "API", "machine learning", "TPU", "Mozilla", "AI", "ML", "REST API", "XGBoost", "R", "Redis"]
["AI", "machine learning", "ML", "XGBoost", "TPU", "Redis", "R", "Go", "API", "REST API"]
Abhishree is a budding tech journalist with a UGD in Political Science. In her free time, Abhishree can be found watching French new wave classic films and playing with dogs.