An Empirical Study of Bots in Software Development -- Characteristics and Challenges from a Practitioner's Perspective

Software engineering bots - automated tools that handle tedious tasks - are increasingly used by industrial and open source projects to improve developer productivity. Current research in this area is held back by a lack of consensus of what software engineering bots (DevBots) actually are, what characteristics distinguish them from other tools, and what benefits and challenges are associated with DevBot usage. In this paper we report on a mixed-method empirical study of DevBot usage in industrial practice. We report on findings from interviewing 21 and surveying a total of 111 developers. We identify three different personas among DevBot users (focusing on autonomy, chat interfaces, and "smartness"), each with different definitions of what a DevBot is, why developers use them, and what they struggle with. We conclude that future DevBot research should situate their work within our framework, to clearly identify what type of bot the work targets, and what advantages practitioners can expect. Further, we find that there currently is a lack of general purpose "smart" bots that go beyond simple automation tools or chat interfaces. This is problematic, as we have seen that such bots, if available, can have a transformative effect on the projects that use them.Comment: To be published at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE

Deception against Deception: Toward A Deception Framework for Detection and Characterization of Covert Micro-targeting Campaigns on Online Social Networks

Micro-targeting campaigns on online social networks are an emerging class of social engineering attacks that prime individuals via personalized content for malicious purposes. Detecting micro-targeting campaigns is challenging due to their clandestine nature and the lack of visibility around users’ private communications. Our work aims to devise theories, methods, and tools to detect suspected micro-targeting campaigns. To this end, we propose to design and generate a network of decoy personas with characteristics similar to those of targeted groups in order to trap, engage, and identify micro-targeting campaigns. In this paper, we discuss our motivation to conduct this interdisciplinary research effort and introduce our focal research questions and preliminary design for a network of decoy personas

Design Perspective on the Role of Advanced Bots for Self-Guided Learning

Virtual worlds are rapidly gaining acceptance in educational settings; with bots play an important role in these environments to help learners. Authentic learning can be significantly supported by bots to help self-guided learning in authentic tasks. in this paper, we investigate what is stopping educators from making more use of bots as a valuable resource and how these barriers can be overcome. This exploratory research uses interviews with six educators, who use educational bots. We show that while the experts have 'big plans' for bot use, the current educational implementations are 'low-level' and restrictive in their application. There is further confusion about appropriate pedagogical models and how to use them effectively as more than 'prompters' or 'extras'. While creation- and control-technologies are advancing, allowing use of bots as a 'hard technology' to guide learners through routine procedures; there is a lack of resources for automation as intelligence technologies are slower to develop and may required future partnerships with external parties before they are available useable by general educators

Improving conversations with digital assistants through extracting, recommending, and verifying user inputs

Digital assistants, including chat bots and voice assistants, suffer from discrepancies and uncertainty in human text and speech inputs. Human dialogue is often varied, ambiguous, and inconsistent, making data entry prone to error and difficult for digital assistants to process. Finding and extracting pertinent information from unstructured user inputs improves and expands the use of digital assistants on any platform. By confirming data entries and providing relevant recommendations when invalid information is provided, the digital assistant enables the use of natural language and introduces a higher degree of flow into the conversation.This paper describes a series of input logic codifiers that form a corrective method to overcome errors and ambiguity typical of voice and text inputs. When users make a common mistake or forget data, the digital assistant can bridge the gap by recommending the most similar data that is available. The assistant measures the delta between the user’s utterance and valid entries using fuzzy logic to identify the closest and next closest data that relates to the unstructured text.Furthermore, there are endless ways to denote dates, locations, etc., making it difficult for digital assistants to extract accurate and relevant data from the user’s natural language. However, the assistant may infer the desired data format or reference from the dialogue provided and validate this with the user as a follow-on question. The desired data format or type is inferred using fuzzy extraction methods, such as fuzzy date extraction, to isolate the desired data format from the unstructured text. This extracted information is then verified or confirmed by the user to maintain data accuracy and avoid downstream data quality issues

UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems

Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-coveted property, especially when it comes to the multiple sources involved in the dialogue system. To better plan and incorporate the use of multiple sources in generating personalized response, we firstly decompose it into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation. We then propose a novel Unified Multi-Source Retrieval-Augmented Generation system (UniMS-RAG) Specifically, we unify these three sub-tasks with different formulations into the same sequence-to-sequence paradigm during the training, to adaptively retrieve evidences and evaluate the relevance on-demand using special tokens, called acting tokens and evaluation tokens. Enabling language models to generate acting tokens facilitates interaction with various knowledge sources, allowing them to adapt their behavior to diverse task requirements. Meanwhile, evaluation tokens gauge the relevance score between the dialogue context and the retrieved evidence. In addition, we carefully design a self-refinement mechanism to iteratively refine the generated response considering 1) the consistency scores between the generated response and retrieved evidence; and 2) the relevance scores. Experiments on two personalized datasets (DuLeMon and KBP) show that UniMS-RAG achieves state-of-the-art performance on the knowledge source selection and response generation task with itself as a retriever in a unified manner. Extensive analyses and discussions are provided for shedding some new perspectives for personalized dialogue systems

Water privatisation and restructuring in Latin America, 2007

A detailed survey of developments in water in south America, including the retreat of the multinationals, the development of public sector alternatives, and persistent problems with compensation claims and development bank policies