Generating a class integration test order (CITO) is essential to reduce the overhead of test stub construction (the primary cost in integration testing) and to ensure system reliability in complex software systems. Although reinforcement learning (RL) has shown promise in automating CITO generation, existing methods suffer from unstable policy learning and limited robustness against structural perturbations and defect injection. These challenges stem from insufficient reward shaping and the lack of reliable oracles for validation. To address these limitations, we propose LM-CITO, a stability-aware RL framework that integrates Lyapunov-guided reward shaping with semantic validation through metamorphic testing (MT). Specifically, we design a Lyapunov energy function over class dependency graphs to promote monotonic structural convergence during training, and define metamorphic relations (MRs) to verify behavioral consistency under controlled perturbations. Extensive experiments on six real-world systems demonstrate that LM-CITO consistently produces more effective policies, yielding CITOs with significantly reduced stubbing costs compared to baseline models. Furthermore, MT verifies the capability of our MRs to detect defects in 19 injected bug variants, confirming the robustness of LM-CITO under various fault-induced perturbations. These results highlight the synergy of stability guidance and MR-based validation, offering an effective, principled solution for oracle-free RL in software testing.

Class Integration Test Order (CITO) generation is essential to minimize testing cost in object-oriented software.Traditional methods based on static dependencies often producesuboptimal results, while recent approaches that incorporatedynamic dependencies typically neglect accurate stubbing costestimation and face scalability challenges. We propose DyCITO+,an extension of DyCITO, which originally modeled CITO generationas a Reinforcement Learning (RL) problem using Qlearning.However, DyCITO relies on tabular methods, and thislimits its scalability. DyCITO+ addresses this by introducingthree Deep Reinforcement Learning (DRL) algorithms: DeepQ-Network (DQN), Proximal Policy Optimization (PPO), andAdvantage Actor-Critic (A2C), to handle the complexity oflarge-scale systems more effectively. DyCITO+ builds on thedynamic dependency analysis mechanism from DyCITO, whichcaptures more accurate runtime relationships, including interfaceimplementation, abstract class inheritance, method overriding,and multilevel inheritance. We evaluated DyCITO+ on eightJava programs of varying sizes. The results show that DyCITO+significantly improves the scalability and effectiveness of CITOgeneration. Among the three DRL methods, A2C consistentlyproduces the lowest overall stubbing complexity, particularly inmedium- and large-scale systems.

The generation of class integration test orders (CITOs) is a pivotal element in integration testing, which focuses on determining the optimal order for integrating classes while testing an object-oriented system. Due to a high number of dependencies and their possible error proneness, some classes are more critical than others in a program. Existing methods for handling these classes only assess risk in terms of their dependencies; they do not consider historical bug information as an additional indicator and mainly work on small programs. To overcome these limitations, this paper introduces Criticality-Driven CITO (CD-CITO) generation, an innovative approach to optimize CITOs by focusing on class criticality. CD-CITO assesses both the importance of a class in terms of its dependencies and the likelihood of defects, based on historical bug data, to determine a criticality score. Then, it reformulates the CITO generation problem as a Reinforcement learning (RL) task and uses the Advantage Actor-Critic (A2C) algorithm to address it. We propose a novel reward calculation strategy to guide the learning agent, balancing stubbing costs with the criticality values of classes to optimize the test order. To extract fault proneness information and assess the approach, the paper uses Defects4J, a data set that contains real bugs and patches of Java programs. The results obtained show that CD-CITO effectively identifies and prioritizes highly critical classes and also minimizes stubbing costs while generating CITOs, which makes it a valuable tool for integration testing.

In recent years, many intelligent optimization algorithms have been applied to the class integration and test order (CITO) problem. These algorithms also have been proved to be able to efficiently solve the problem. Here, the design of fitness function is a key task to generate the optimal solution. To better solve the class integration and test order problem, we propose a new fitness function to generate the optimal solution that achieves a balanced compromise between the different measures (objectives) such as the total number of stubs and the total stubbing complexity in this paper. We used some programs to compare and evaluate the different approaches. The experimental results show that our proposed approach is encouraging to some extent in solving the class integration and test order problem.