Robotic Process Automation: a review of organizational grey literature

Research on Robotic Process Automation (RPA) in the last decade has increased but lags behind developments in practice. This study explores the definition, evolution and categories of RPA, its benefits and challenges, identifies guidelines for implementation and provides a future outlook. Since there is an evident scarcity of comprehensive grey literature reviews in the area, this study presents an extensive narrative review of organizational grey literature on RPA by analyzing sixty-one organizational reports and white papers published between 2015 and 2020. This study provides a unified definition of RPA and groups the many categories of RPA into three types: basic automation, cognitive automation, and artificial intelligence. The study identifies the benefits of RPA and categorizes them into monetary; simplicity; efficiency and productivity; flexibility and scalability; reliability and consistency; compliance and governance; customer satisfaction; employee efficiency; and other long-term organizational benefits. The main challenges of RPA are awareness and perception of RPA; uncertainty about how to prepare for RPA; change management challenges while implementing RPA; and challenges associated with RPA vendors. Three main steps of RPA implementation are highlighted. This study provides practitioners and researchers with an extensive bird’s eye insight into RPA from an industry perspective

Robotic Process Automation: a review of organizational grey literature

Research on Robotic Process Automation (RPA) in the last decade has increased but lags behind developments in practice. This study explores the definition, evolution and categories of RPA, its benefits and challenges, identifies guidelines for implementation and provides a future outlook. Since there is an evident scarcity of comprehensive grey literature reviews in the area, this study presents an extensive narrative review of organizational grey literature on RPA by analyzing sixty-one organizational reports and white papers published between 2015 and 2020. This study provides a unified definition of RPA and groups the many categories of RPA into three types: basic automation, cognitive automation, and artificial intelligence. The study identifies the benefits of RPA and categorizes them into monetary; simplicity; efficiency and productivity; flexibility and scalability; reliability and consistency; compliance and governance; customer satisfaction; employee efficiency; and other long-term organizational benefits. The main challenges of RPA are awareness and perception of RPA; uncertainty about how to prepare for RPA; change management challenges while implementing RPA; and challenges associated with RPA vendors. Three main steps of RPA implementation are highlighted. This study provides practitioners and researchers with an extensive bird’s eye insight into RPA from an industry perspective

Nudge: Accelerating Overdue Pull Requests Towards Completion

Pull requests are a key part of the collaborative software development and code review process today. However, pull requests can also slow down the software development process when the reviewer(s) or the author do not actively engage with the pull request. In this work, we design an end-to-end service, Nudge, for accelerating overdue pull requests towards completion by reminding the author or the reviewer(s) to engage with their overdue pull requests. First, we use models based on effort estimation and machine learning to predict the completion time for a given pull request. Second, we use activity detection to reduce false positives. Lastly, we use dependency determination to understand the blocker of the pull request and nudge the appropriate actor(author or reviewer(s)). We also do a correlation analysis to understand the statistical relationship between the pull request completion times and various pull request and developer related attributes. Nudge has been deployed on 147 repositories at Microsoft since 2019. We do a large scale evaluation based on the implicit and explicit feedback we received from sending the Nudge notifications on 8,500 pull requests. We observe significant reduction in completion time, by over 60%, for pull requests which were nudged thus increasing the efficiency of the code review process and accelerating the pull request progression

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns

Promises and Perils of Mining Software Package Ecosystem Data

The use of third-party packages is becoming increasingly popular and has led to the emergence of large software package ecosystems with a maze of inter-dependencies. Since the reliance on these ecosystems enables developers to reduce development effort and increase productivity, it has attracted the interest of researchers: understanding the infrastructure and dynamics of package ecosystems has given rise to approaches for better code reuse, automated updates, and the avoidance of vulnerabilities, to name a few examples. But the reality of these ecosystems also poses challenges to software engineering researchers, such as: How do we obtain the complete network of dependencies along with the corresponding versioning information? What are the boundaries of these package ecosystems? How do we consistently detect dependencies that are declared but not used? How do we consistently identify developers within a package ecosystem? How much of the ecosystem do we need to understand to analyse a single component? How well do our approaches generalise across different programming languages and package ecosystems? In this chapter, we review promises and perils of mining the rich data related to software package ecosystems available to software engineering researchers.Comment: Submitted as a Book Chapte

A systematic literature review on Robotic Process Automation security

The technocrat epoch is overflowing with new technologies and such cutting-edge facilities accompany the risks and pitfalls. Robotic process automation is another innovation that empowers the computerization of high-volume, manual, repeatable, everyday practice, rule-based, and unmotivating human errands. The principal objective of Robotic Process Automation is to supplant monotonous human errands with a virtual labor force or a computerized specialist playing out a similar work as the human laborer used to perform. This permits human laborers to zero in on troublesome undertakings and critical thinking. Robotic Process Automation instruments are viewed as straightforward and strong for explicit business process computerization. Robotic Process Automation comprises intelligence to decide if a process should occur. It has the capability to analyze the data presented and provide a decision based on the logic parameters set in place by the developer. Moreover, it does not demand for system integration, like other forms of automation. Be that as it may since the innovation is yet arising, the Robotic Process Automation faces a few difficulties during the execution

Design of network monitoring system based on LibreNMS using Line Notify, Telegram, and Email notification

Institut Teknologi Telkom Jakarta (IT Telkom Jakarta) is an educational institution that supports student activities and provides internet capabilities to implement online learning systems. As the number of students increases with every year, so does the use of the internet and intranet networks and the experienced network problems. A network administrator is a person who is responsible for managing a computer network. Network administrators usually face network problems in monitoring network devices. This is because the process and operation are done manually. This means network administrators need direct access to the location to monitor all resources. Therefore, a network device monitoring system is needed to manage network devices centrally. This research focuses on the problem of monitoring network devices using open-source tools and software. Based on the implementation results, free network monitoring software such as LibreNMS can track and monitor all devices in all conditions and notify the active device condition in case of network failure such as up, down, reboot to the administrator via Line Notify, Telegram, and Email. With this network monitoring system, IT Telkom Jakarta is expected to be able to implement an integrated and well-monitored internet network system. Besides, the results of this study also produce real-time data on bandwidth usage, logging problems, and resource availability. This can significantly improve network availability and security

Risks of Robotic Process Automation: A multivocal literature review

In recent years, many companies from different sectors have chosen to support digital transformation in process automation using RPA. In fact, it can be seen that automations have been revolutionising and benefiting the human workforce by minimising repetitive tasks subjective to errors and maximising the technical and operational efficiency of companies. However, it is not without its risks, since it is based on robots devoid of any critical thinking. Thus, the present research focuses on a case study on RPA risks, in which an in-depth analysis was conducted through an MLR with 107 documents gathered and thoroughly examined throughout the community, including books, scientific articles, technical reports, conferences, among others. This research contributes a list of a total of 88 risks organized, mapped and grouped among 9 categories. In this sense, this study will assist future researchers to identify RPA risks in order to define actions to avoid negative impacts.Nos últimos anos, muitas empresas de diferentes setores optaram por apoiar a transformação digital na automação de processos usando RPA. De facto, é possível verificar que as automações têm vindo a revolucionar e beneficiar a força do trabalho humano minimizando as tarefas repetitivas subjetiveis a erros e maximizando a eficiência técnica e operacional das empresas. No entanto, não deixa de ter os seus riscos, uma vez que se fundamenta em robôs desprovidos de qualquer pensamento crítico. Assim, a presente investigação incide num estudo de caso sobre os riscos de RPA, no qual se realizou uma análise profunda, através de um MLR com 107 documentos reunidos e minuciosamente examinados em toda a comunidade, incluindo livros, artigos científicos, relatórios técnicos, conferências, entre outros. Esta investigação contribui com uma lista de um total de 88 riscos organizados, mapeados e agrupados entre 9 categorias. Nesse sentido, este estudo auxiliará futuros investigadores a identificar os riscos de RPA de forma a definirem ações que evitem impactos negativos