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.

LLMs demonstrate significant potential across various software engineering tasks. However, they still face challenges in generating correct code on the first attempt when addressing complex requirements. Introducing a discriminator to select reliable outputs from multiple generated results is an effective way to enhance their reliability and stability. Currently, these discriminators fall into two categories: execution-based discriminators and non-execution-based discriminators. Execution-based discriminators face flexibility challenges due to difficulties in obtaining test cases and security concerns, while non-execution-based discriminators, although more flexible, struggle to capture subtle differences in code details. To maintain flexibility while improving the model’s ability to capture fine-grained code details, this paper proposes Condor. We first design contrastive learning to optimize the code representations of the base model, enabling it to reflect differences in code details. Then, we leverage intermediate data from the code modification process to further enrich the discriminator’s training data, enhancing its ability to discern code details. Experimental results indicate that on the subtle code difference dataset (i.e., CodeNanoFix), Condor significantly outperforms other discriminators in discriminative performance: Condor (1.3B) improves the discriminative F1 score of DeepSeek-Coder (1.3B) from 67% to 73%. In discriminating LLM-generated outputs, Condor (1.3B) and Condor (110M) raise the Pass@1 score of Llama-3.1-Instruct (70B) on the CodeNanoFix dataset from 52.64% to 62.63% and 59.64%, respectively. Moreover, Condor demonstrates strong generalization capabilities on the APPS, MBPP, and LiveCodeBench datasets. For example, Condor (1.3B) improves the Pass@1 of Llama-3.1-Instruct (70B) on the APPS dataset by 147.05%.

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.