# Automating the Analysis of Negative Test Verdicts:
## 4 Conclusions  
The aim of this research was to design and test a method to integrate artificial intelligence algorithms into the embedded software testing process at the phase of verification and validation of the results. This phase represents a critical constraint, requiring the involvement of specialized experts. The authors are convinced that by applying artificial intelligence algorithms, it is feasible to optimize this process and significantly shorten it. In the perspective of real industrial projects, which are very often developed using agile methodologies, thus ensuring minimum time intervals for the various stages, this is of great importance. The authors in their study examined the behavior of both unsupervised and supervised algorithms. From an industrial point of view, it appeared that unsupervised algorithms would be the preferred solution. However, the research carried out on data collected from a real test system showed that adjusting the parameters of the algorithms to ensure that the results obtained from them are sufficient requires time and the involvement of an artificial intelligence expert for each embedded system under test. From an implementation perspective, this limitation makes it a solution that does not provide the expected optimization of costs and human resources. In this contribution, we also presented the experimental results of several selected supervised algorithms on the same data from a real test system. The best results were obtained for MLP and SVM algorithms. A low MSE error value was observed for them (MLP\(-\)0.4% and SVM\(-\)0.2%), demonstrating the high accuracy of the regression predictions. Lower performance of the MLP algorithm on the training set suggests potential over-fitting or high sensitivity to training data. The SVM algorithm performed better in this case, which is characterized by being resistant to overfitting by minimizing the length of the weight vector. Analyzing the confusion matrix, a conclusion emerged that the SVM algorithm is the one that demonstrates the most efficient classification of negative instances and the ability of the model to successfully classify negative instances in unseen data. The application of supervised algorithms requires more involvement in the processing of the training data, which has to be labeled. However, this can be performed by the same engineer who develops the test cases. Knowledge or experience of artificial intelligence is not required, only familiarity with the functioning of the test system itself and the test environment. This is a necessary stage that can easily be automated by creating an expertise database of rules for specific groups of projects. The benefits of automating the analysis of negative test verdicts are various. First, it accelerates the testing process, enabling earlier feedback and faster debugging. This, in turn, boosts product development cycles and release time. Second, it mitigates the risk of human error, increasing the reliability and consistency of test results. Third, it optimizes resource allocation, allowing for testing teams to focus their expertise on more complex and strategic tasks. Finally, it supports a data-driven approach to testing, utilizing the power of big data analytics to uncover insights that might otherwise remain hidden. Conceptualization, A.G.-G. and P.P. ; methodology, A.G.-G.; software, A.G.-G.; validation, A.G.-G. and P.P. ; formal analysis, A.G.-G.; investigation, A.G.-G.; resources, A.G.-G.; data curation, A.G.-G.; writing--original draft preparation, A.G.-G.; writing--review and editing, P.P. ; visualization, A.G.-G.; supervision, P.P. ; project administration, A.G.-G.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript. Research supported by the Polish Ministry of Education and Science Grant No DWD/4/55/2020. This publication is supported by the statutory funds of the Department of Fundamentals of Machinery Design. Not applicable. Data Availability Statement: Restrictions apply to the availability of these data.