Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice

• Due to the migration megatrend, efficient and effective second-language acquisition is vital. One proposed solution involves AI-enabled conversational agents for person-centered interactive language practice. We present results from ongoing action research targeting quality assurance of proprietary generative dialog models trained for virtual job interviews. The action team elicited a set of 38 requirements for which we designed corresponding automated test cases for 15 of particular interest to the evolving solution. Our results show that six of the test case designs can detect meaningful differences between candidate models. While quality assurance of natural language processing applications is complex, we provide initial steps toward an automated framework for machine learning model selection in the context of an evolving conversational agent. Future work will focus on model selection in an MLOps setting.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

Nyckelord

action research

AI quality

conversational agent

generative dialog model

requirements engineering

software testing

Learning algorithms

Natural language processing systems

Quality assurance

Software agents

Conversational agents

Dialogue models

Megatrends

Model Selection

Requirement engineering

Software testings

Swedishs

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)