| 1. |
|
|
| 2. |
- Bastajic, Milos, et al.
(författare)
-
Operationalizing Machine Learning Using Requirements-Grounded MLOps
- 2024
-
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 14588 LNCS, s. 231-248
-
Konferensbidrag (refereegranskat)abstract
- [Context & Motivation] Machine learning (ML) use has increased significantly, [Question/Problem] however, organizations still struggle with operationalizing ML. [Principle results] In this paper, we explore the intersection between machine learning operations (MLOps) and Requirements engineering (RE) by investigating the current problems and best practices associated with developing an MLOps process. The goal is to create an artifact that would guide MLOps implementation from an RE perspective, aiming for a more systematic approach to managing ML models in production by identifying and documenting the goals and objectives. The study adopted a Design Science Research methodology, examining the difficulties currently faced in creating an MLOps process, identified potential solutions to these difficulties, and assessed the effectiveness of one particular solution, an artifact containing guiding Requirements Questions sorted by ML stages and practitioner roles. [Contribution] By establishing a more thorough understanding of how the two domains interact and by offering practical guidance for implementing MLOps processes from an RE perspective, this study advances both the MLOps and RE fields.
|
|
| 3. |
- Habibullah, Khan Mohammad, et al.
(författare)
-
Requirements and software engineering for automotive perception systems: an interview study
- 2024
-
Ingår i: REQUIREMENTS ENGINEERING. - 0947-3602 .- 1432-010X.
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 4. |
- Holtmann, Jörg, 1979, et al.
(författare)
-
Using boundary objects and methodological island (BOMI) modeling in large-scale agile systems development
- 2024
-
Ingår i: Software and Systems Modeling. - 1619-1374 .- 1619-1366. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- Large-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. Here, there is a conflict between coordination and group autonomy, and it is challenging to determine what necessary coordination information must be shared by what teams or groups, and what can be left to local team management. We introduce a way to manage this complexity using a modeling framework based on two core concepts: methodological islands (i.e., groups using different development methods than the surrounding organization) and boundary objects (i.e., artifacts that create a common understanding across team borders). However, we found that companies often lack a systematic way of assessing coordination issues and the use of boundary objects between methodological islands. As part of an iterative design science study, we have addressed this gap by producing a modeling framework (BOMI: Boundary Objects and Methodological Islands) to better capture and analyze coordination and knowledge management in practice. This framework includes a metamodel, as well as a list of bad smells over this metamodel that can be leveraged to detect inter-team coordination issues. The framework also includes a methodology to suggest concrete modeling steps and broader guidelines to help apply the approach successfully in practice. We have developed Eclipse-based tool support for the BOMI method, allowing for both graphical and textual model creation, and including an implementation of views over BOMI instance models in order to manage model complexity. We have evaluated these artifacts iteratively together with five large-scale companies developing complex systems. In this work, we describe the BOMI framework and its iterative evaluation in several real cases, reporting on lessons learned and identifying future work. We have produced a matured and stable modeling framework which facilitates understanding and reflection over complex organizational configurations, communication, governance, and coordination of knowledge artifacts in large-scale agile system development.
|
|
| 5. |
- Kumar Pandey, Sushant, 1990, et al.
(författare)
-
Design Patterns Understanding and Use in the Automotive Industry: An Interview Study
- 2024
-
Ingår i: Lecture Notes in Computer Science. - 0302-9743 .- 1611-3349. ; 14483, s. 301-319
-
Konferensbidrag (refereegranskat)abstract
- Automotive software is increasing in complexity, leading to new challenges for designers and developers. Design patterns, which offer reusable solutions to common design problems, are a potential way to deal with this complexity. Although design patterns have received much focus in academic publications, it is not clear how they are used in practice. This paper presents an interview-based study that explores the use of design patterns in the automotive industry. The study findings reveal how automotive practitioners view and use design patterns in their software designs. Our study revealed that industry experts have a view of design patterns which often differs from the academic views. They use design patterns in combination with architecture guidelines, principles, and frameworks. Instead of the academic focus on the design patterns, industry professionals focus on the design, architectural tactics, and standards. Such findings highlight the need for a more nuanced understanding of the concept and practical applications of design patterns within the context of industrial software engineering practices.
|
|
