Robotic Process Automation (RPA) has gained widespread adoption in corporate organizations, streamlining work processes while also introducing additional maintenance tasks. Effective governance of RPA can be achieved through the reusability of RPA components. However, refactoring RPA processes poses challenges when dealing with larger development teams, outsourcing, and staff turnover. This research aims to explore the possibility of identifying similarities in RPA processes for refactoring. To address this issue, we have developed Similarity Discovering Techniques for RPA (SiDiTeR). SiDiTeR utilizes source code or process logs from RPAautomations to search for similar or identical parts within RPA processes. The techniques introduced are specifically tailored to the RPA domain. We have expanded the potential matches by introducing a dictionary feature which helps identify different activities that produce the same output, and this has led to improved results in the RPA domain. Through our analysis, we have discovered 655 matches across 156 processes, with the longest match spanning 163 occurrences in 15 processes. Process similarity within the RPA domain proves to be a viable solution for mitigating the maintenance burden associated with RPA. This underscores the significance of process similarity in the RPA domain.

Companies that use robotic process automation very often deal with problems maintaining the bots in their RPA portfolio. Current key performance indicators do not track the behavior of RPA bots or processes. For better maintainability of RPA bots, it is crucial to easily identify problematic behavior in RPA bots. Therefore, we propose a strategy that tracks and measures the behavior of processes to increase the maintainability of RPA bots. We selected indicators of statistical dispersion for measuring variability to analyze the behavior of RPA bots. We analyzed how well statistical dispersion can describe the behavior of RPA bots on 12 processes. The results provide evidence that, by using statistical dispersion for behavioral analysis, the unwanted behavior of RPA bots can be described. Our results showed that statistical dispersion can describe the success rate with a correlation of -0.91 and outliers in the data with a correlation of 0.42. Also, the results demonstrate that the outliers do not influence the success rate of RPA bots. This research implies that we can describe the behavior of RPA bots with variable analysis. Furthermore, with high probability, it can also be used for analyzing other processes, as a tool for gaining insights into performance and as a benchmark tool for comparing or selecting a process to rework.

Robotic process automation security has become a topic of increasing importance for organizations looking to implement RPA on a wide scale. Streamlining your business with robotic process automation (RPA) helps your business automate mundane, redundant tasks by doing them quicker, more efficiently, and cheaper. But, with RPA implementation comes the chance of additional security risks. Robotic Process Automation (RPA) has quickly become an important form of business process automation. In practice, RPA allows bots--specially designed software programs--to take over several different complex processes to efficiently perform mundane or redundant tasks normally performed by people.

Information Services Group (ISG) reported in 2018 that 92% of companies were aiming to adopt robotic process automation (RPA) by 2020 because they wanted to increase operational efficiencies. This large number reflects how eager companies are to automate routine business processes. One of the easiest places to employ RPA is in very simple, highly repetitive business processes that rely on transactional data that comes in fixed record lengths, with data fields always in the same locations. This data is highly predictable, and automation tools like RPA that depend on recognizing repetitive data patterns are in strong positions to excel. However, even the most routine business process consists of unstructured and semi-structured big data, as well as the more traditional fixed record data.