Context: Chatbots emerged in the decade of 60 and are currently a popular type of application that provides the experience of talking to a human-like robot. However, the similarity of their behaviour to a human is also what makes the elicitation and documentation of conversational-related requirements a hard task, due to the subjective nature of conversations. Currently there are no standards or guidelines to register and test subjective requirements, present in human conversations. Methods: We proposed a metamodel for documentation of conversational requirements and conducted the validation through a 2-step questionnaire. We then analysed the feedback received and applied improvements in the model instances. Results: Based on metamodel we propose the conversational integrated map, an artifact that correlates the subjective requirements with their associated KPIs. Validation was conducted through a simulation with 4 practitioners in a 2-step questionnaire. Artifact format choice revealed a preference for mainly textual and table models. Scalability and necessary time to complete were points of divergence between answers. Conclusion: Proposed artifact was well accepted and could provide pre-made models available for edition for ease of use. Future steps of this study include the elaboration of a guide of best practices on chatbot documentation.
In today's technology-driven world, there is a growing interest in leveraging Artificial Intelligence (AI) to streamline software testing processes. Our research delves into GUI-based testing, a prominent technique for verifying software functionality. Preliminary findings from our industrial survey of 45 respondents provide insights into the use of AI in GUI-based software testing. The survey aims to understand how AI supports GUI-based testing, the AI techniques and tools used, and the perceived advantages and limitations. The collected results suggest a diffuse yet superficial utilization of AI-based mechanisms among GUI-based testers. Practitioners often employ AI techniques in a technology-agnostic way, treating commercial tools as black boxes. These findings underscore the need for additional research aimed at gaining a deeper understanding of the AI techniques and tools employed in industry and their intended purposes.
Interpreted programming languages, like Python, are amongst the most popular programming languages. This, combined with high developer efficiency leads to many web-application backends and web-services that are written in Python. While it is known that that interpreted languages like Python are way less energy efficient compared to compiled languages like , Rust, etc., little is known about the energy efficiency of various versions of Python interpreters. In this paper, we study via a controlled lab experiment the energy consumption of various versions of the Python interpreter CPython when running a server-side rendered web-application. Our results indicate that currently the most energy efficient version is CPython 3.12. Energy consumption of CPython 3.12 can drop by more than 8
Recent years have seen an increase of decision-making software based on Machine Learning (ML). Multiple cases have been reported where such software are discriminatory based on attributes such as race and gender. Thus, ML-based decision making software need to be tested for discrimination, or fairness testing. One popular approach to fairness testing is to find discriminatory data items by first conducting a global search, and then searching locally near the found discriminatory data items. Aequitas, CGFT, and KOSEI are three examples taking this approach. However, there are issues in terms of stability and efficiency. We thus propose an approach called Robust Search Using T-way Testing (RSUTT), which is based on CGFT for global search and KOSEI for local search. Experiments showed that RSUTT performs more efficiently compared to Aequitas, CGFT, and KOSEI.
The digital society is characterised by a continuous presence of digital systems being pure software systems or cyber-physical systems, which help and support humans in their life activities. Humans can be passive or active, the interaction can be implicit or explicit, and they can be more or less conscious of the presence of digital systems. However, the impact of the digital systems on social, economic, and political spheres is clear and risky. This is also testified by, e.g., the recent AI Act of the European Community. In this paper, we analyse whether modern systems require a new understanding of quality and, consequently, new quality dimensions. We challenge the ISO/IEC 25010 quality standard with new emerging stakeholders needs, and we pave the way for a reconsideration of the standard and the traditional understanding of quality to include and consider also these new emerging needs.
