Integration testing is a very important step in software testing. Existing methods evaluate the stubbing cost for class inte-gration test orders by considering only the interclass direct relationships such as inheritance, aggregation, and associa-tion, but they omit the interclass indirect relationship caused by control coupling, which can also affect the test orders and the stubbing cost. In this paper, we introduce an integration test order strategy to consider control coupling. We ad-vance the concept of transitive relationship to describe this kind of interclass dependency and propose a new measure-ment method to estimate the complexity of control coupling, which is the complexity of stubs created for a transitive rela-tionship. We evaluate our integration test order strategy on 10 programs on various scales. The results show that consid-ering the transitive relationship when generating class integration test orders can significantly reduce the stubbing cost for most programs and that our integration test order strategy obtains satisfactory results more quickly than other methods.

REpresentation State Transfer (REST) is an architectural style for designing web applications that enables scalable, stateless communication between clients and servers via common HTTP techniques. Web APIs that employ the REST style are known as RESTful (or REST) APIs. When using or testing a RESTful API, developers often refer to its specifications, which are typically defined by open-source standards such as the OpenAPI Specification (OAS). However, writing and updating these specifications can be very time-consuming and error-prone, which can negatively affect the use of RESTful APIs, especially when software requirements change. Many tools and methods have been proposed to solve this problem, including Respector and Swagger Core. OAS generation can be regarded as a common text-generation task that produces a formal description of API endpoints from source code. A potential approach for this may involve using Large Language Models (LLMs), which have strong capabilities in both code understanding and text generation. Motivated by this, we propose a novel approach to generate the OASs of RESTful APIs using LLMs: LLM-based RESTful API-Specification Generation (LRASGen). To the best of our knowledge, this is the first technique to use LLMs to generate OASs directly from RESTful API source code. Compared with existing tools and methods, LRASGen can even generate OASs when the implementation is incomplete (e.g., with partial code, missing annotations/comments, etc.). To evaluate LRASGen's performance, we conducted a series of empirical studies on 53 real-world RESTful APIs, identifying the following entities: endpoint-methods, endpoint-parameter, parameter-constraint, and endpoint-response. These APIs were developed in five programming languages using ten frameworks. The results show that LRASGen can generate accurate specifications, and can cover an average of 43.54% more entities than the developer-provided specifications. LRASGen also outperforms the current state-of-the-art technique, Respector, across all evaluated APIs, on four identification tasks: LRASGen achieved performance improvements of 54.17% in endpoint-method identification; 28.3% in endpoint-parameter identification; 98.6% in parameter-constraint identification; and 74.8% in endpoint-response identification.

Mobile Graphical User Interface (GUI) testing is a critical component of quality assurance for mobile applications. As GUI designs grow increasingly complex, vision-based testing methods have become essential for improving the quality of test scripts and reports. However, current approaches face significant limitations. For instance, the process of cropping GUI widgets requires significant manual effort and consumes considerable time. Meanwhile, existing automated testing tools still fail to generate satisfactory test sequences. In this paper, we propose Vision-based Test Sequence Generation (VTSG), a novel perception-driven approach for mobile GUI testing. More specifically: (1) VTSG employs a light-weight GUI element detection model to crop widgets from GUI pages automatically; (2) Guided by human perception principles, it sequences widget screenshots through saturation and spatial layout analysis; (3) The system then integrates GUI widget interaction instructions to generate vision-based test scripts that accurately simulate human interaction patterns on mobile devices. We evaluate VTSG against four state-of-the-art (SOTA) GUI testing tools across five applications. Experimental results demonstrate that VTSG significantly outperforms existing methods, achieving 47.44% code coverage and 51.33% activity coverage, respectively, compared to the other approaches. Additionally, we conduct a series of supplementary experiments on two mainstream commercial applications (i.e., Toutiao and Douyin). The results further confirm that VTSG maintains higher activity coverage even on these real-world commercial apps.

Similarity-based test case prioritization algorithms have been applied to regression testing. The common characteristic of these algorithms is to reschedule the execution order of test cases according to the distances between pair-wise test cases. The distance information can be calculated by different similarity measures. Since the topologies vary with similarity measures, the distances between pair-wise test cases calculated by different similarity measures are different. Similarity measures could significantly influence the effectiveness of test case prioritization. Therefore, we empirically evaluate the effects of six similarity measures on two similarity-based test case prioritization algorithms. The obtained results are statistically analyzed to recommend the best combination of similarity-based prioritization algorithms and similarity measures. The experimental results, confirmed by a statistical analysis, indicate that Euclidean distance is more efficient in finding defects than other similarity measures. The combination of the global similarity-based prioritization algorithm and Euclidean distance could be a better choice. It generates not only higher fault detection effectiveness but also smaller standard deviation. The goal of this study is to provide practical guides for picking the appropriate combination of similarity-based test case prioritization techniques and similarity measures.