Understanding and Managing Non-functional Requirements for Machine Learning Systems
Licentiate thesis, 2023

Background: Machine Learning (ML) systems learn using big data and solve a wide range of prediction and decision making problems that would be difficult to solve with traditional systems. However, increasing use of ML in complex and safety-critical systems has raised concerns about quality requirements, which are defined as Non-Functional requirements (NFRs). Many NFRs, such as fairness, transparency, explainability, and safety are critical in ensuring the success and acceptance of ML systems. However, many NFRs for ML systems are not well understood (e.g., maintainability), some known NFRs may become more important (e.g., fairness), while some may become irrelevant in the ML context (e.g., modularity), some new NFRs may come into play (e.g., retrainability), and the scope of defining and measuring NFRs in ML systems is also a challenging task.

Objective: The research project focuses on addressing and managing issues related to NFRs for ML systems. The objective of the research is to identify current practices and challenges related to NFRs in an ML context, and to develop solutions to manage NFRs for ML systems.

Method: We are using design science as a base of the research method. We carried out different empirical methodologies–including interviews, survey, and a part of systematic mapping study to collect data, and to explore the problem space. To get in-depth insights on collected data, we performed thematic analysis on qualitative data and used descriptive statistics to analyze qualitative data. We are working towards proposing a quality framework as an artifact to identify, define, specify, and manage NFRs for ML systems.

Findings: We found that NFRs are crucial and play an important role for the success of the ML systems. However, there is a research gap in this area, and managing NFRs for ML systems is challenging. To address the research objectives, we have identified important NFRs for ML systems, and NFR and NFR measurement-related challenges. We also identified preliminary NFR definition and measurement scope and RE-related challenges in different example contexts.

Conclusion: Although NFRs are very important for ML systems, it is complex and difficult to define, allocate, specify, and measure NFRs for ML systems. Currently the industry and research is does not have specific and well organized solutions for managing NFRs for ML systems because of unintended bias, the non-deterministic behavior of ML, and expensive and time-consuming exhaustive testing. Currently, we are working on the development of a quality framework to manage (e.g., identify important NFRs, scoping and measuring NFRs) NFRs in the ML systems development process.

Requirements Engineering

Quality Requirements

Machine Learning

NFRs

Non-functional Requirements

CSE Jupiter 473
Opponent: Angelo Susi, Senior Researcher, Head of Software Engineering Unit, Foundation Bruno Kessler (FBK), Trento, Italy

Author

Khan Mohammad Habibullah

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest

International Workshop on Software Engineering for Responsible Artificial Intelligence,;(2022)p. 29-36

Paper in proceeding

Non-functional requirements for machine learning: understanding current use and challenges in industry

Requirements Engineering for Automotive Perception Systems: An Interview Study

Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges

Subject Categories

Software Engineering

Computer Science

Publisher

Chalmers

CSE Jupiter 473

Online

Opponent: Angelo Susi, Senior Researcher, Head of Software Engineering Unit, Foundation Bruno Kessler (FBK), Trento, Italy

More information

Latest update

5/12/2023