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We recently proposed inline tests for validating individual program statements; they allow developers to provide test inputs, expected outputs, and test oracles immediately after a target statement. But, existing code can have many target statements. So, automatic generation of inline tests is an important next step towards increasing their adoption. We propose ExLi, the first technique for automatically generating inline tests. ExLi extracts inline tests from unit tests; it first records all variable values at a target statement while executing unit tests. Then, ExLi uses those values as test inputs and test oracles in an initial set of generated inline tests. Target statements that are executed many times could have redundant initial inline tests. So, ExLi uses a novel coverage-and-mutation based reduction process to remove redundant inline tests. We implement ExLi for Java and use it to generate inline tests for 718 target statements in 31 open-source programs. ExLi reduces 17,273 initially generated inline tests to 905 inline tests. The final set of generated inline tests kills up to 25.1% more mutants than developer written and automatically generated unit tests. That is, ExLi generates inline tests that can improve the fault-detection capability of the test suites from which they are extracted. 

Regression test selection (RTS) speeds up regression testing by only re-running tests that might be affected by code changes. Ideal RTS safely selects all affected tests and precisely selects only affected tests. But, aiming for this ideal is often slower than re-running all tests. So, recent RTS techniques use program analysis to trade precision for speed, i.e., lower regression testing time, or even use machine learning to trade safety for speed. We seek to make recent analysis-based RTS techniques more precise, to further speed up regression testing. Independent studies suggest that these techniques reached a “performance wall” in the speed-ups that they provide. We manually inspect code changes to discover those that do not require re-running tests that are only affected by such changes. We categorize 29 kinds of changes that we find from five projects into 13 findings, 11 of which are semantics-modifying. We enhance two RTS techniques—Ekstazi and STARTS—to reason about our findings. Using 1,150 versions of 23 projects, we evaluate the impact on safety and precision of leveraging such changes. We also evaluate if our findings from a few projects can speed up regression testing in other projects. The results show that our enhancements are effective and they can generalize. On average, they result in selecting 41.7% and 31.8% fewer tests, and take 33.7% and 28.7% less time than Ekstazi and STARTS, respectively, with no loss in safety. 

We present pytest-inline, the first inline testing framework for Python. We recently proposed inline tests to make it easier to test individual program statements. But, there is no framework-level support for developers to write inline tests in Python. To fill this gap, we design and implement pytest-inline as a plugin for pytest, the most popular Python testing framework. Using pytest-inline, a developer can write an inline test by assigning test inputs to variables in a target statement and specifying the expected test output. Then, pytest-inline runs each inline test and fails if the target statement’s output does not match the expected output. In this paper, we describe our design of pytest- inline, the testing features that it provides, and the intended use cases. Our evaluation on inline tests that we wrote for 80 target statements from 31 open-source Python projects shows that using pytest-inline incurs negligible overhead, at 0.012x. pytest-inline is integrated into the pytest-dev organization, and a video demo is at https://www.youtube.com/watch?v=pZgiAxR_uJg. 

Unit tests are widely used to check source code quality, but they can be too coarse-grained or ill-suited for testing individual program statements. We introduce inline tests to make it easier to check for faults in statements. We motivate inline tests through several language features and a common testing scenario in which inline tests could be beneficial. For example, inline tests can allow a developer to test a regular expression in place. We also define language-agnostic requirements for inline testing frameworks. Lastly, we implement I-Test, the first inline testing framework. I-Test works for Python and Java, and it satisfies most of the requirements. We evaluate I-Test on open-source projects by using it to test 144 statements in 31 Python programs and 37 Java programs. We also perform a user study. All nine user study participants say that inline tests are easy to write and that inline testing is beneficial. The cost of running inline tests is negligible, at 0.007x -- 0.014x, and our inline tests helped find two faults that have been fixed by the developers. 

Regression testing - rerunning tests at each code version to detect newly-broken functionality - is important and widely practiced. But regression testing is costly due to the large number of tests and high frequency of code changes. Regression test selection (RTS) optimizes regression testing by rerunning only a subset of tests that can be affected by code changes. Researchers showed that RTS based on dynamic and static program analysis can save substantial testing time for (medium-sized) open-source projects. Simultaneously, practitioners showed that RTS based on machine learning (ML) is lightweight and works well on very large software repositories, e.g., in Facebook’s monorepository. We combine analysis-based RTS and ML-based RTS by using ML-based RTS to choose a subset of tests selected by analysis-based RTS. To do so, we first design several novel ML-based RTS techniques that leverage mutation analysis to obtain a training set for learning the impact of code changes on test outcomes. Then, we empirically evaluate, using 10 projects, the benefits of combining various ML models with analysis-based RTS. We also compare combining the techniques with using each technique individually. Combining ML-based RTS with two analysis-based RTS techniques - Ekstazi and STARTS - selects 25.34% and 21.44% fewer tests.