Regression testing is part of every testing phase in software testing and plays a key role in software quality assurance. With the increasing complexity of software systems, it is often necessary to design test cases for regression testing to balance test adequacy and test overhead. Some studies have proposed algorithms for sequence completion, which generates only one test sequence to be completed at a time, generating test sequences with excessive overheads, and the method relies on a large amount of data flow information, and the degree of automation is also not high. To this end, this paper proposes a coverage criterion based on ‘test obligation’, which integrates the control flow and data flow information of the EFSM model as the coverage basis, and is used to guide the test sequence generation work to better reflect the dependency relationships on the EFSM, and accurately determine the range of the regression test. Based on the proposed test obligation coverage criterion, a genetic algorithm for regression testing test sequence generation is designed and implemented by combining test obligation coverage diversity and test sequence length as a fitness function. Experiments show that the test sequence set generated by this algorithm is better than the existing methods in terms of test sequence length and number of test sequences, which effectively reduces the regression test overhead and improves the test sequence generation efficiency.

Specification based testing is a common method in software testing. Specification based testing is divided into behavior-based testing, such as finite state machine method; and input-based testing, such as random testing, combination testing, decision table testing, equivalence class testing, boundary value testing method and so on. At present, there are many modeling methods for behavior-based testing and test cases can be generated automatically through these models. However, for these input-based testing methods, test cases are generated manually by searching for parameters and parameter values, which is too inefficient. To solve this problem, we want to improve the efficiency of input-based testing methods by automatically generating test cases. Therefore, this paper presents a software input space model that can represent parameter values and constraint relationships as the basis of automatic generation of test cases. Some safety-critical software tests need to consider the input timing of parameters, so we also express the input timing of parameters in the input space model. Then we propose the process of modeling the input space, which is finally expressed in the form of a directed graph. The values of parameters and the input timing of parameters are expressed on the edges of the directed graph, and the constraints between parameters are described by the way of node splitting, and all valid test sequences are represented by the path traversing the graph. Finally, the proposed input space modeling method is applied to a real test project to demonstrate the feasibility of the method.

Extended Finite State Machine(EFSM) has a wide range of application systems and protocols in control modeling. Genetic algorithms are commonly used in EFSM test data generation due to their good performance. Studies have been done to generate regression test sequences to meet the requirements based on changes in EFSM, there is a large amount of historical test data to choose from when using genetic algorithms to generate test data for this part of the test sequence, and there is no research on how to fully utilize the existing test data to generate regression test data for the EFSM. In this paper, a multi-population genetic algorithm is designed to generate test data to meet the requirements. Firstly, some test data that can cover the regression test sequences are selected from the historical test data, and for the rest of the test sequences, a mathematical model for the simultaneous optimization of multiple test sequences is constructed, secondly, in order to make full use of the historical test data, the relationship between the existing test data and the target test sequences is investigated, and the initial population is constructed based on the similarity of the test sequences and the FSCS-ART algorithm, which makes full use of the The initial population is constructed based on the similarity of test sequences and the FSCS-ART algorithm, which makes full use of the existing test data and ensures the diversity of the initial population. Finally, the adaptive crossover operator and variation operator were designed and the fitness function was optimized. The experimental results show the feasibility and efficiency of this method in test data generation.

Software testing is an indispensable part of the software life cycle, and the quality of software testing largely affects the quality of software delivered to users. However, in the current stage of software testing quality research, the focus on testing quality influencing factors is still limited to theoretical analysis and model application, and there is a lack of empirical research based on real data and samples. To address this problem, this paper proposes the idea of using natural experiments to conduct empirical research in the field of software testing quality for the first time, and analyzes the feasibility of the research proposal through literature research. In this paper, the empirical study was conducted using real data provided by enterprises, through the research of cooperative enterprise project data and event records, selected the "CNAS expansion of the on-site review and audit training" as an exogenous event, team capacity as the explanatory variable to build a natural experimental model of test quality. After completing the empirical model, we analyze the results, which show that the exogenous events have a significant disposition effect on test quality, and the empirical results pass the four commonly used robustness tests, indicating that the experimental results have a high 95% confidence level. In addition, this paper also analyzes the control variables in the empirical model and finds that test team size can have an impact on test quality by affecting test diversity, which provides ideas for subsequent research. Finally, based on the experimental ideas and empirical results of this study, this paper summarizes the methodological paradigm of applying the natural experiment method to empirically study and analyze the factors affecting test quality, which provides an important reference example for future empirical studies by introducing the natural experiment method in the study of software quality and test quality, and greatly expands the research horizon of related fields.