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- Habibullah, Khan Mohammad, et al.
(author)
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Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest
- 2022
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In: Proceedings - Workshop on Software Engineering for Responsible AI, SE4RAI 2022. - New York, NY, USA : ACM. ; , s. 29-36
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Conference paper (peer-reviewed)abstract
- Systems that rely on Machine Learning (ML systems) have differing demands on quality - non-functional requirements (NFRs) - compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope - and of the extent of existing research - is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results - as well as inter-coder reliability on a sample of NFRs - to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain.CCS CONCEPTS • Software and its engineering → Extra-functional properties; Requirements analysis; • Computing methodologies → Machine learning.
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| 2. |
- Habibullah, Khan Mohammad, et al.
(author)
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Non-functional requirements for machine learning: understanding current use and challenges among practitioners
- 2023
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In: Requirements Engineering. - : Springer Science and Business Media LLC. - 0947-3602 .- 1432-010X. ; 28, s. 283-316
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Journal article (peer-reviewed)abstract
- Systems that rely on Machine Learning (ML systems) have differing demands on quality—known as non-functional requirements (NFRs)—from traditional systems. NFRs for ML systems may differ in their definition, measurement, scope, and comparative importance. Despite the importance of NFRs in ensuring the quality ML systems, our understanding of all of these aspects is lacking compared to our understanding of NFRs in traditional domains. We have conducted interviews and a survey to understand how NFRs for ML systems are perceived among practitioners from both industry and academia. We have identified the degree of importance that practitioners place on different NFRs, including cases where practitioners are in agreement or have differences of opinion. We explore how NFRs are defined and measured over different aspects of a ML system (i.e., model, data, or whole system). We also identify challenges associated with NFR definition and measurement. Finally, we explore differences in perspective between practitioners in industry, academia, or a blended context. This knowledge illustrates how NFRs for ML systems are treated in current practice, and helps to guide future RE for ML efforts.
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| 3. |
- Habibullah, Khan Mohammad, et al.
(author)
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Requirements and software engineering for automotive perception systems: an interview study
- 2024
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In: REQUIREMENTS ENGINEERING. - 0947-3602 .- 1432-010X. ; 29:1, s. 25-48
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Journal article (peer-reviewed)abstract
- Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and-most importantly-safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems.
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| 4. |
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| 5. |
- Habibullah, Khan Mohammad, 1990, et al.
(author)
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Requirements Engineering for Automotive Perception Systems: An Interview Study
- 2023
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. - 9783031297854 ; 13975 LNCS, s. 189-205
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Conference paper (peer-reviewed)abstract
- Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that practitioners experience in this domain. Method: We have conducted an interview study with 19 participants across five companies and performed thematic analysis. Results: Practitioners have difficulty specifying upfront requirements, and often rely on scenarios and operational design domains (ODDs) as RE artifacts. Challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Conclusions: Our findings contribute to understanding how RE is practiced for DAS perception systems and the collected challenges can drive future research for DAS and other ML-enabled systems.
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| 6. |
- Habibullah, Khan Mohammad, 1990
(author)
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Understanding and Managing Non-functional Requirements for Machine Learning Systems
- 2023
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Licentiate thesis (other academic/artistic)abstract
- 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.
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| 7. |
- Heyn, Hans-Martin, 1987, et al.
(author)
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Automotive Perception Software Development : An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
- 2023
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In: Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350301137 ; , s. 13-24
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Conference paper (peer-reviewed)abstract
- Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, requires large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations.This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving.
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