GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps. However, since different apps may implement the same functionality in different ways, direct mapping may result in incomplete or buggy test cases, thus significantly impacting the effectiveness of testing the target functionality and the practical applicability of migration approaches. In this article, we propose a new migration paradigm (i.e., the abstraction-concretization paradigm) that first abstracts the test logic for the target functionality and then utilizes this logic to generate the concrete GUI test case. Furthermore, we introduce MACdroid , the first approach that migrates GUI test cases based on this paradigm. Specifically, we propose an abstraction technique that utilizes source test cases from source apps targeting the same functionality to extract a general test logic for that functionality. Then, we propose a concretization technique that utilizes the general test logic to guide an LLM in generating the corresponding GUI test case (including events and assertions) for the target app. We evaluate MACdroid on two widely used datasets (including 31 apps, 34 functionalities, and 123 test cases). On the FrUITeR dataset, the test cases generated by MACdroid successfully test 64% of the target functionalities, improving the baselines by 191%. On the Lin dataset, MACdroid successfully tests 75% of the target functionalities, outperforming the baselines by 42%. These results underscore the effectiveness of MACdroid in GUI test migration.

Prevalent Fault Localization (FL) techniques rely on tests to localize buggy program elements. Tests could be treated as fuel to further boost FL by providing more debugging information. Therefore, it is highly valuable to measure the Fault Diagnosis Capability (FDC) of a test for diagnosing faults, so as to select or generate tests to better help FL (i.e., FL-oriented test selection or FL-oriented test generation). To this end, researchers have proposed many FDC metrics, which serve as the selection criterion in FL-oriented test selection or the fitness function in FL-oriented test generation. Existing FDC metrics can be classified into result-agnostic and result-aware metrics depending on whether they take test results (i.e., passing or failing) as input. Although result-aware metrics perform better in test selection, they have restricted applications due to the input of test results, e.g., they cannot be applied to guide test generation. Moreover, all the existing FDC metrics are designed based on some pre-defined heuristics and have achieved limited FL performance due to their inaccuracy. To address these issues, in this article, we reconsider result-agnostic metrics (i.e., metrics that do not take test results as input), and propose a novel result-agnostic metric RLFDC which predicts FDC values of tests through reinforcement learning. In particular, we treat FL results as reward signals, and train an FDC prediction model with the direct FL feedback to automatically learn a more accurate measurement rather than design one based on pre-defined heuristics. Finally, we evaluate the proposed RLFDC on Defects4J by applying the studied metrics to test selection and generation. According to the experimental results, the proposed RLFDC outperforms all the result-agnostic metrics in both test selection and generation, e.g., when applied to selecting human-written tests, RLFDC achieves 28.2% and 21.6% higher acc@1 and mAP values compared to the state-of-the-art result-agnostic metric TfD. Besides, RLFDC even achieves competitive performance compared to the state-of-the-art result-aware metric FDG in test selection.

GUI test case migration is to migrate GUI test cases from a source app to a target app. The key of test case migration is widget matching. Recently, researchers have proposed various approaches by formulating widget matching as a matching task. However, since these matching approaches depend on static word embeddings without using contextual information to represent widgets and manually formulated matching functions, there are main limitations of these matching approaches when handling complex matching relations in apps. To address the limitations, we propose the first learning-based widget matching approach named TEMdroid (TEst Migration) for test case migration. Unlike the existing approaches, TEMdroid uses BERT to capture contextual information and learns a matching model to match widgets. Additionally, to balance the significant imbalance between positive and negative samples in apps, we design a two-stage training strategy where we first train a hard-negative sample miner to mine hard-negative samples, and further train a matching model using positive samples and mined hard-negative samples. Our evaluation on 34 apps shows that TEM-droid is effective in event matching (i.e., widget matching and target event synthesis) and test case migration. For event matching, TEM-droid's Top1 accuracy is 76%, improving over 17% compared to baselines. For test case migration, TEMdroid's F1 score is 89%, also 7% improvement compared to the baseline approach.

GUI test case migration is the process of migrating GUI test cases from a source app to a target app for a specific functionality. However, test cases obtained via existing migration approaches can hardly be directly used to test target functionalities and typically require additional manual modifications. This problem may significantly impact the effectiveness of testing target functionalities and the practical applicability of migration approaches. In this paper, we propose MigratePro, the first approach to enhancing GUI test case migration via synthesizing a new test case based on multiple test cases for the same functionality migrated from various source apps to the target app. The aim of MigratePro is to produce functional test cases with less human intervention. Specifically, given multiple migrated test cases for the same functionality in the target app, MigratePro first combines all the GUI states related to these migrated test cases into an overall state-sequence. Then, MigratePro organizes events and assertions from migrated test cases according to the overall state-sequence and endeavors to remove the should-be-removed events and assertions, while also incorporating some connection events in order to make the should-be-included events and assertions executable. Our evaluation on 30 apps, 34 functionalities, and 127 test cases shows that MigratePro improves the capability of three representative migration approaches (i.e., Craftdroid, AppFlow, ATM), successfully improving testing the target functionalities by 86%, 333%, and 300%, respectively. These results underscore the generalizability of MigratePro for effectively enhancing migration approaches.