Context-based counselor agent for software development ecosystem

• Published in: Computing Vol. 97; no. 1; pp. 3 - 28
• Main Authors: Shinozaki, Tetsuo, Yamamoto, Yukiko, Tsuruta, Setsuo
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.01.2015; Springer Nature B.V
• Subjects: Analysis; Artificial Intelligence; Collaboration; Computer Appl. in Administrative Data Processing; Computer Communication Networks; Computer Science; Conversation; Counseling; Ecosystems; Employment; Human; Information Systems Applications (incl.Internet); Information technology; Knowledge; Natural language; Ontology; Personnel; Professionals; Reflection; Software; Software development; Software Engineering; Studies; Success; Vibration; 68T35 Languages and softwares; Self-awareness; Conversational agent; 68T10 Pattern recognition; speech recognition; Ontology; Digital ecosystem; Reflection; Context-based
• ISSN: 0010-485X; 1436-5057
• DOI: 10.1007/s00607-013-0352-y

Counseling for information technology (IT) personnel lies at the intersection between the software development ecosystem where IT employees collaborate professionally and the social ecosystem where they communicate with each other to share the success or handle the failure of software development. Today, counseling has become a major issue in the IT industry, since the success rate of IT system development projects is as low as 30 %, and more than 60 % of IT professionals suffer from anxiety or other emotional problems. This paper describes a conversational agent aiming to replace human counselors assisting IT personnel in software development ecosystems toward future deployment to social ecosystems. Utilizing IT domain ontology knowledge, our agent automatically adapts the vocabulary used in its responses according to the context and to the current phase of the conversation. Using context-based reflection support knowledge, the agent generates its response consisting of (1) chatterbot-like mirroring/rewording for context sharing and (2) newly proposed context-respectful mechanism of prompts for “context narrowing/digging” to help a client discover problems and become aware of their solutions via deep reflections of IT personnel undergoing counseling. Knowledge focusing on a single domain, such as IT counseling domain, and context-based/context-respectful reflection allow our counseling agent to work properly without having to acquire and manage a huge amount of knowledge. Experimental results show that clients interact with our agent on average two times longer than they do with ELIZA-style conversational agents; also, a questionnaire-based validation has shown the average value of questionnaire’s result was “agree” side for our agent, but “disagree” side for ELIZA-style conversational agents. Therefore, the user acceptance level of our agent is much higher than that of conventional chatterbots.