| 1. |
- Dzhusupova, Rimman, et al.
(författare)
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Challenges in developing and deploying AI in the engineering, procurement and construction industry
- 2022
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Ingår i: Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022. - : IEEE. ; , s. 1070-1075
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Konferensbidrag (refereegranskat)abstract
- AI in the Engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-depth studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.
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| 2. |
- Bosch, Jan, 1967, et al.
(författare)
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Ecosystem traps and where to find them
- 2018
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Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 30:11
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Tidskriftsartikel (refereegranskat)abstract
- Today, companies operate in business ecosystems where they collaborate, compete, share, and learn from others with benefits such as to present more attractive offerings and sharing innovation costs. With ecosystems being the new way of operating, the ability to strategically reposition oneself to increase or shift power balance is becoming key for competitive advantage. However, companies run into a number of traps when trying to realize strategical changes in their ecosystems. In this paper, we identify 5 traps that companies fall into. First, the “descriptive versus prescriptive trap” is when companies assume that current boundaries between partners are immutable. Second, the “assumptions trap” is when powerful ecosystem partners assume that they understand what others regard as value-adding without validating their assumptions. Third, the “keeping it too simple trap” is when companies overlooks the effort required to align interests. Fourth, the “doing it all at once trap” is when companies disrupt an ecosystem assuming that all partners can change direction at the same time. Finally, the “planning trap” is when companies are unable to move forward without a complete plan. We provide empirical evidence for each trap, and we propose an ecosystem engagement process for how to avoid falling into these.
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| 3. |
- Bosch, Jan, 1967, et al.
(författare)
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It takes three to tango: Requirement, outcome/data, and AI driven development
- 2018
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS.org. - 1613-0073. ; 2305, s. 177-192, s. 177-192
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Konferensbidrag (refereegranskat)abstract
- Today’s software-intensive organizations are experiencing a paradigm-shift with regards to how to develop software systems. With the increasing availability and access to data and with artificial intelligence (AI) and technologies such as machine learning and deep learning emerging, the traditional requirement driven approach to software development is becoming complemented with other approaches. In addition to having development teams executing on requirements specified by product management, the development of software systems is progressing towards a data driven practice where teams receive an outcome to realize and where design decisions are taken based on continuous collection and analysis of data. On top of this, and due to artificial intelligence components being introduced to more and more software systems, learning algorithms, automatically generated models and data is replacing code and the development process is no longer only a manual effort but instead a combination of human and automated processes. In this paper, and based on multi-case study research in embedded systems and online companies, we see that companies use different approaches to software development but that they often take a requirement driven approach even if they would benefit from one of the other two. Also, we see that picking the wrong approach results in a number of problems such as e.g. inefficiency and waste of development efforts. To help address these problems, we develop a holistic development framework and we provide guidelines on how to improve effectiveness in development. The contribution of this paper is two-fold. First, we identify that there are three distinct approaches to software development; (1) Requirement driven development, (2) Outcome/data driven development and (3) AI driven development and we outline the typical problems that companies experience when using the wrong approach for the wrong purpose. Second, we provide a holistic framework with guidelines for when to use what approach to software development.
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| 4. |
- Dzhusupova, Rimman, et al.
(författare)
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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects
- 2022
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Ingår i: Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022. - New York, NY, USA : ACM. ; , s. 124-135
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Konferensbidrag (refereegranskat)abstract
- Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.
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| 5. |
- Fabijan, Aleksander, et al.
(författare)
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Experimentation growth: Evolving trustworthy A/B testing capabilities in online software companies
- 2018
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Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 30:12
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Tidskriftsartikel (refereegranskat)abstract
- Companies need to know how much value their ideas deliver to customers. One of the most powerful ways to accurately measure this is by conducting online controlled experiments (OCEs). To run experiments, however, companies need to develop strong experimentation practices as well as align their organization and culture to experimentation. The main objective of this paper is to demonstrate how to run OCEs at large scale using the experience of companies that succeeded in scaling. Based on case study research at Microsoft, Booking.com, Skyscanner, and Intuit, we present our main contribution-The Experiment Growth Model. This four-stage model addresses the seven critical aspects of experimentation and can help companies to transform their organizations into learning laboratories where new ideas can be tested with scientific accuracy. Ultimately, this should lead to better products and services.
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| 6. |
- Figalist, Iris, et al.
(författare)
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Fast and curious: A model for building efficient monitoring- and decision-making frameworks based on quantitative data
- 2021
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Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849 .- 1873-6025. ; 132
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Tidskriftsartikel (refereegranskat)abstract
- Context: Nowadays, the hype around artificial intelligence is at its absolute peak. Large amounts of data are collected every second of the day and a variety of tools exists to enable easy analysis of data. In practice, however, making meaningful use of it is way more challenging. For instance, affected stakeholders often struggle to specify their information needs and to interpret the results of such analyses. Objective: In this study we investigate how to enable continuous monitoring of information needs, and the generation of knowledge and insights for various stakeholders involved in the lifecycle of software-intensive products. The overarching goal is to support their decision making by providing relevant insights related to their area of responsibility. Methods: We implement multiple monitoring- and decision-making frameworks for six individual, real-world cases selected from three different platforms and covering four types of stakeholders. We compare the individual procedures to derive a generic process for instantiating such frameworks as well as a model to scale it up for multiple stakeholders. Results: For one, we discovered that information needs of stakeholders are often related to a limited subset of data sources and should be specified in stages. For another, stakeholders often benefit from sharing and reusing existing components among themselves in later phases. Specifically, we identify three types of reuse: (1) Data and knowledge, (2) tools and methods, and (3) concepts. As a result, key aspects of our model are iterative feedback and specification cycles as well as the reuse of appropriate components to speed up the instantiation process and maximize the efficiency of the model. Conclusion: Our results indicate that knowledge and insights can be generated much faster and stakeholders feel the benefits of the analysis very early on by iteratively specifying information needs and by systematically sharing and reusing knowledge, tools and concepts.
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| 7. |
- Figalist, Iris, et al.
(författare)
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Scaling Agile Beyond Organizational Boundaries: Coordination Challenges in Software Ecosystems
- 2019
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Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 355, s. 189-206, s. 189-206
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Konferensbidrag (refereegranskat)abstract
- The shift from sequential to agile software development originates from relatively small and co-located teams but soon gained prominence in larger organizations. How to apply and scale agile practices to fit the needs of larger projects has been studied to quite an extent in previous research. However, scaling agile beyond organizational boundaries, for instance in a software ecosystem context, raises additional challenges that existing studies and approaches do not yet investigate or address in great detail. For that reason, we conducted a case study in two software ecosystems that comprise several agile actors from different organizations and, thereby, scale development across organizational boundaries, in order to elaborate and understand their coordination challenges. Our results indicate that most of the identified challenges are caused by long communication paths and a lack of established processes to facilitate these paths. As a result, the participants in our study, among others, experience insufficient responsivity, insufficient communication of prioritizations and deliverables, and alterations or loss of information. As a consequence, agile practices need to be extended to fit the identified needs.
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| 8. |
- Fredriksson, Teodor, 1992, et al.
(författare)
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Data Labeling: An Empirical Investigation into Industrial Challenges and Mitigation Strategies
- 2020
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 12562 LNCS, s. 202-216, s. 202-216
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Konferensbidrag (refereegranskat)abstract
- Labeling is a cornerstone of supervised machine learning. However, in industrial applications, data is often not labeled, which complicates using this data for machine learning. Although there are well-established labeling techniques such as crowdsourcing, active learning, and semi-supervised learning, these still do not provide accurate and reliable labels for every machine learning use case in the industry. In this context, the industry still relies heavily on manually annotating and labeling their data. This study investigates the challenges that companies experience when annotating and labeling their data. We performed a case study using a semi-structured interview with data scientists at two companies to explore their problems when labeling and annotating their data. This paper provides two contributions. We identify industry challenges in the labeling process, and then we propose mitigation strategies for these challenges.
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| 9. |
- Issa Mattos, David, 1990, et al.
(författare)
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An activity and metric model for online controlled experiments
- 2018
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11271 LNCS, s. 182-198, s. 182-198
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Konferensbidrag (refereegranskat)abstract
- Accurate prioritization of efforts in product and services development is critical to the success of every company. Online controlled experiments, also known as A/B tests, enable software companies to establish causal relationships between changes in their systems and the movements in the metrics. By experimenting, product development can be directed towards identifying and delivering value. Previous research stresses the need for data-driven development and experimentation. However, the level of granularity in which existing models explain the experimentation process is neither sufficient, in terms of details, nor scalable, in terms of how to increase number and run different types of experiments, in an online setting. Based on a case study of multiple products running online controlled experiments at Microsoft, we provide an experimentation framework composed of two detailed experimentation models focused on two main aspects; the experimentation activities and the experimentation metrics. This work intends to provide guidelines to companies and practitioners on how to set and organize experimentation activities for running trustworthy online controlled experiments.
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| 10. |
- Issa Mattos, David, 1990, et al.
(författare)
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Challenges and strategies for undertaking continuous experimentation to embedded systems: Industry and research perspectives
- 2018
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Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 314, s. 277-292, s. 277-292
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Konferensbidrag (refereegranskat)abstract
- Context: Continuous experimentation is frequently used in web-facing companies and it is starting to gain the attention of embedded systems companies. However, embedded systems companies have different challenges and requirements to run experiments in their systems. Objective: This paper explores the challenges during the adoption of continuous experimentation in embedded systems from both industry practice and academic research. It presents strategies, guidelines, and solutions to overcome each of the identified challenges. Method: This research was conducted in two parts. The first part is a literature review with the aim to analyze the challenges in adopting continuous experimentation from the research perspective. The second part is a multiple case study based on interviews and workshop sessions with five companies to understand the challenges from the industry perspective and how they are working to overcome them. Results: This study found a set of twelve challenges divided into three areas; technical, business, and organizational challenges and strategies grouped into three categories, architecture, data handling and development processes. Conclusions: The set of identified challenges are presented with a set of strategies, guidelines, and solutions. To the knowledge of the authors, this paper is the first to provide an extensive list of challenges and strategies for continuous experimentation in embedded systems. Moreover, this research points out open challenges and the need for new tools and novel solutions for the further development of experimentation in embedded systems.
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