# Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning
## 2 Related Work  
This section discusses the literature on the TSP and various related techniques proposed by the scientific community. [PERSON] and [PERSON] [1] presented multiple criteria-based techniques using the additional strategy for test case prioritization in their research. One is recognized as primary criteria, and the other one is recognized as secondary criteria. Primary criteria prioritize the test cases, and the secondary criteria break the attachment between the test cases when more than one test case gives the same coverage in the first criteria. [PERSON] presented a new fixed-strength interaction coverage-based prioritization (FICBP) method [11]. At the time of prioritization, authors frequently use base choice coverage so that it can enhance testing efficiency. The algorithm chooses those test cases covered by the greater one-wise factor level combination. These  
Figure 1: Conceptual model of the proposed test suite prioritization mechanism. include test cases that have not been considered in the base choice coverage. The evaluation is performed in terms of the time complexity of the FICBP algorithm. The authors in [9] discussed the test-suite reduction and prioritization framework called test optimizer. The test optimizer framework is based on the CC. The test optimizer consists of a test planner and a test manager. The test leader provides inputs to the test planner, and the inputs are allotted optimization time, test suites, and test repository. The test manager generates the test cases using coverage-based test suite reduction and test-suite prioritization techniques by observing the allocated optimization time. The results are then stored in the test repository. The timer and optimizer are the main sub-components of the test manager. Test case prioritization can also be accomplished using the cuckoo search (CS) algorithm [8]. The CS algorithm is based on the nature of cuckoo birds. The CS algorithm prioritizes the test cases on fault statistics, such as the more significant number of fault detection in less time. Artificial bee colony (ABC) is also used in [4] for TSP. Moreover, swarm intelligence (SI) have been adopted to prioritize test cases based on CC. SI or particle swarm optimization (PSO) are algorithms developed based on biologically inspired artificial intelligence models of social insects, such as bees and ants [4]. [PERSON] [15] used RL for test case prioritization and test case selection. In [15], the researchers introduced a method called RETECS for learning test case prioritization and test case selection in continuous integration (CI) to reduce the round trip time between the developer feedback and code commits on failed test cases. RETECS uses RL to prioritize and select test cases based on the last execution, failure history, and duration. In [5], the researcher presented a test case selection technique for applications connected to databases. [PERSON] and [PERSON] presented unsupervised clustering with unit tests and random values to regulate those test cases associated with modifications and connected to databases [5]. The authors in [14] proposed a novel approach called REPiR for TSP. The REPiR approach combines regression test prioritization (RTP) and information retrieval that primarily deals with the ranking problems. RTP analyzes the test cases written in a programming language, and IR looks into the documents written in natural language. [PERSON] [26] presented an adaptive random sequences clustering technique. This technique uses white box information such as CC and black box information such as input data for testing and the expected output against input values. In [31], the researcher presented ant colony optimization (ACO) for TSP, and that technique is acquired from natural ant behavior. Their technique considers test cases and faults related to software as an input with the objective function. The ants navigate from the initial fault node to the final fault node are chosen randomly between ants.