One major challenge of regression testing are flaky tests, i.e., tests that may pass in one run but fail in another run for the same version of code. One prominent category of flaky tests is order-dependent (OD) flaky tests, which can pass or fail depending on the order in which the tests are run. To help developers debug and fix OD tests, prior work attempts to automatically find OD-relevant tests, which are tests that determine whether an OD test passes or fails, depending on whether the OD-relevant tests run before or after the OD test. Prior work found OD-relevant tests by running different tests before the OD test, without considering each test's likelihood of being OD-relevant tests. We propose RankF to rank tests in order of likelihood of being OD-relevant tests, finding the first OD-relevant test for a given OD test more quickly. We propose two ranking approaches, each requiring different information. Our first approach,$\boldsymbol{RankF}_L$, relies on training a large-language model to analyze test code. Our second approach,$\boldsymbol{RankF}_O$, relies on analyzing prior test-order execution information. We evaluate our approaches on 155 OD tests across 24 open-source projects. We compare RankF against baselines from prior work, where we find that RankF finds the first OD-relevant test for an OD test faster than the best baseline; depending on the type of OD-relevant test, RankF takes 9.4 to 14.1 seconds on median, compared to the baseline's 34.2 to 118.5 seconds on median.

Continuous development (CD) helps developers quickly release and update their software. To enact CD, developers customize their CD builds to perform several tasks, including compiling, testing, static analysis checks, etc. However, as developers add more tasks to their builds, the builds take longer to run, therefore slowing down the entire CD process. Furthermore, developers may unknowingly include tasks into their builds whose results are not used (e.g., generating coverage files that are never read or uploaded anywhere), therefore wasting build runtime doing unnecessary tasks. We propose OptCD, a technique to dynamically detect unnecessary work within CD builds. Our intuition is that unnecessary work can be identified by the generation of files that are not used by any other task within the build. OptCD runs alongside a CD build, tracking the generated files during the build and which files are read/written. Files that are written to but are never read from are unnecessary content from a build. Based on the names of the unnecessary files, OptCD then maps the files to the specific build tasks responsible for generating or writing to those files. Finally, OptCD leverages ChatGPT to suggest changing the build configuration to disable generating these unnecessary files. Our evaluation of OptCD on 22 open-source projects finds that 95.6% of projects generate at least one unused directory, a directory whose contents are all unnecessarily generated. OptCD identifies the correct task that generates 92.0% of the unused directories. Further, OptCD can produce a patch for the CD configuration file to prevent generating 72.0% of the unused directories. Using the patches, we reduce the runtime by 7.0% on average for the projects we studied. We submitted 26 pull requests for the unused directories that we could disable. Developers have accepted 12 of them, with five rejected, and nine still pending.