Software testing is one of the costliest stages in the software development life cycle. One approach to reducing the test execution cost is to group changes and test them as a batch (i.e. batch testing). However, when tests fail in a batch, commits in the batch need to be re-tested to identify the cause of the failure, i.e. the culprit commit. The re-testing is typically done through bisection (i.e. a binary search through the commits in a batch). Intuitively, the effectiveness of batch testing highly depends on the size of the batch. Larger batches require fewer initial test runs, but have a higher chance of a test failure that can lead to expensive test re-runs to find the culprit. We are unaware of research that investigates and simulates the impact of batch sizes on the cost of testing in industry. In this work, we first conduct empirical studies on the effectiveness of batch testing in three large-scale industrial software systems at Ericsson. Using 9 months of testing data, we simulate batch sizes from 1 to 20 and find the most cost-effective BatchSize for each project. Our results show that batch testing saves 72% of test executions compared to testing each commit individually. In a second simulation, we incorporate flaky tests that pass and fail on the same commit as they are a significant source of additional test executions on large projects. We model the degree of flakiness for each project and find that test flakiness reduces the cost savings to 42%. In a third simulation, we guide bisection to reduce the likelihood of batch-testing failures. We model the riskiness of each commit in a batch using a bug model and a test execution history model. The risky commits are tested individually, while the less risky commits are tested in a single larger batch. Culprit predictions with our approach reduce test executions up to 9% compared to Ericsson’s current bisection approach. The results have been adopted by developers at Ericsson and a tool to guide bisection is in the process of being added to Ericsson’s continuous integration pipeline.