Chatbot Quality Assurance Using RPA

Chatbots are becoming mainstream consumer engagement tools, and well-developed chatbots are already transforming user experience and personalization. Chatbot Quality Assurance (QA) is an essential part of the development and deployment process, regardless of whether it’s conducted by one entity (business) or two (developers and business), to ensure ideal results. Robotic Process Automation (RPA) can be explored as a potential facilitator to improve, augment, streamline, or optimize chatbot QA. RPA is ideally suited for tasks that can be clearly defined (rule-based) and are repeating in nature. This limits its ability to become an all-encompassing technology for chatbot QA testing, but it can still be useful in replacing part of the manual QA testing of chatbots. Chatbot QA is a complex domain in its own right and has its own challenges, including the lack of streamlined/standardized testing protocols and quality measures, though traits like intent recognition, responsiveness, conversational flow, etc., are usually tested, especially at the end-user testing phase. RPA can be useful in certain areas of chatbot QA, including its ability to increase the sample size for training and testing datasets, generating input variations, splitting testing/conversation data sets, testing for typo resiliency, etc. The general rule is that the easier a testing process is to clearly define and set rules for, the better it's a candidate for RPA-based testing. This naturally increases the lean towards technical testing and makes it moderately unfeasible as an end-user testing alternative. It has the potential to optimize chatbot QA in conjunction with AI and ML testing tools

Software-based dialogue systems: Survey, taxonomy and challenges

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. The corresponding author gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the inancial support of his predoctoral grant FPI-UPC. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Software engineering for AI-based systems: A survey

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.This work has been partially funded by the “Beatriz Galindo” Spanish Program BEAGAL18/00064 and by the DOGO4ML Spanish research project (ref. PID2020-117191RB-I00)Peer ReviewedPostprint (author's final draft