# Ontology-Based Regression Testing: A Systematic Literature Review
## 4 Results and Discussion
### RQ2: What Are the State-of-the-Art Techniques of Ontology-Based Regression Testing? This section presents an overview of state-of-the-art ontology-based regression testing. The study's inclusion criteria show the chosen studies' year-wise distribution in Figure 3.  
Figure 3 shows that most of the research studies were published in 2011 and 2012, respectively. We can see that the number of research studies on TCP was in limited numbers other than in the years 2011-2012. The research community remained more focused on ontology-based regression testing for test case generation. In the following, we discuss the research studies on the ontologies in regression testing. Researchers in [37] proposed the severity-based TCP technique, which detected more faults than other TCP techniques. However, this study does not discuss the criteria that have been applied to determine fault severity. Therefore, we need to incorporate risk  
Figure 3: Year-wise publications on ontology-based regression testing. Figure 2: Roadmap of regression testing approaches. metrics to make ontology-based TCP more explicit in achieving results. Consequently, we may add new components to the existing testing model [38], which compares existing TCP approaches and their efficiencies. The research studies have widely discussed the probability of running services and related risks [39; 40]. As earlier mentioned in studies, ontologies have been used to represent dependency relationships, i.e., classes and links. Mathematical models have been significantly applied for measuring the failure probabilities of web services. Static web applications that have been updated or modified have been missed regarding the above studies' research efforts. Although the adaptive approach of risk estimation and test case ranking is of great importance, it would be better if researchers discussed static web services regarding ontology-based TCP. The study [41] focused on ontology-driven systems for regression testing. The study involved targeted ontologies rather than using the code of programs. The researchers compared the ontology of the old system to the ontology of the updated or modified system. Thus, the newly added or changed elements of an ontology helped in identifying the potential test cases. In this way, they evaluated the proposed ontology-based regression testing technique by implementing it in the ONTORETEST tool. A graph was built based on the ontologies of old and modified systems [42]. As a result, cost and time factors were reduced from the ontology-based RT approach because this research had executed test cases of only changed parts to identify faults. Inference rules and ontologies present a better solution to testing issues in the area of regression testing. This claim was proved by implementing the inference rules on the Prologue tool's ontologies [35]. Thus, OWL functional tests are translated to the prologue syntax. Researchers compared the manually created test cases to their corresponding requirements and observed inconsistencies. Then, they formulated a set of inference rules in plain English. Ontologies are also used to perform testing in safety-related web systems. The safety analysis report (SAR) document was a credible source for the automated validation of TCG for the nuclear industry systems [43]. Accidents and associated safety measures are crucial elements enclosed in the proposed approach. SAR ontology is applied to the XML markup language and safety analysis markup language (SAML). The latter study's main contribution is developing the TCG tool, which automatically performs addition, deletion, and saving the tags in the markup language. Most critical systems, including the nuclear safety system, need to comply with users' needs. To validate the nuclear system, the study [44] proposed ontology-specific knowledge to validate it systematically. The proposed methodology in the studies, as mentioned earlier, is similar because both studies use SAR to convert scenarios in the processing structures.