| 1. |
- Figalist, Iris, et al.
(författare)
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An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions
- 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. 217-233, s. 217-233
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Konferensbidrag (refereegranskat)abstract
- Nowadays, machine learning (ML) is an integral component in a wide range of areas, including software analytics (SA) and business intelligence (BI). As a result, the interest in custom ML-based software analytics and business intelligence solutions is rising. In practice, however, such solutions often get stuck in a prototypical stage because setting up an infrastructure for deployment and maintenance is considered complex and time-consuming. For this reason, we aim at structuring the entire process and making it more transparent by deriving an end-to-end framework from existing literature for building and deploying ML-based software analytics and business intelligence solutions. The framework is structured in three iterative cycles representing different stages in a model’s lifecycle: prototyping, deployment, update. As a result, the framework specifically supports the transitions between these stages while also covering all important activities from data collection to retraining deployed ML models. To validate the applicability of the framework in practice, we compare it to and apply it in a real-world ML-based SA/BI solution.
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| 2. |
- Figalist, Iris, et al.
(författare)
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Breaking the Vicious Circle : Why AI for software analytics and business intelligence does not take off in practice
- 2020
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Ingår i: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : IEEE. - 9781728195322 - 9781728195339 ; , s. 5-12
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Konferensbidrag (refereegranskat)abstract
- In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often gets stuck in a prototypical stage and the results are rarely used to make decisions based on data. To understand the underlying root causes of this phenomenon, we conduct both an explanatory case study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical analytics to continuous and productively usable software analytics and business intelligence based on AI.
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| 3. |
- Figalist, Iris, et al.
(författare)
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Breaking the vicious circle: A case study on why AI for software analytics and business intelligence does not take off in practice
- 2022
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Ingår i: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often remains at a prototypical stage, and the results are rarely used to make decisions based on data. To understand the underlying causes of this phenomenon, we conduct an explanatory case study consisting of and interview study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical AI-based analytics to continuous and productively usable software analytics and business intelligence solutions. In order to break the vicious circle in a targeted manner, we identify a set of solutions based on existing literature as well as the previously conducted interviews and survey. Finally, these solutions are validated by a focus group of experts.
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| 4. |
- Figalist, Iris, et al.
(författare)
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Business as Unusual: A Model for Continuous Real-Time Business Insights Based on Low Level Metrics
- 2019
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Ingår i: Proceedings - 45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019. - : IEEE. - 9781728134215 ; , s. 66-73
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Konferensbidrag (refereegranskat)abstract
- © 2019 IEEE. A wide variety of tools to monitor and track software systems, such as websites or smartphone applications, during runtime already exists. However, their aggregated results are often not sufficient to answer questions on a product management level since these questions address several levels of complexity and abstractions, and tend to be formulated on a rather high level, for instance concerning the efficiency of their website structure for their users. A straightforward mapping between low level metrics and high level insights is typically not possible. This causes a gap that makes it challenging to continuously provide quantitative high-level insights in real-time. In order to address this challenge, we conducted a study within three distinct platforms and products, and propose a model based on our results. After defining a case for each of the independent platforms and products, we implemented a process to measure high level insights using low level metrics for each of these cases. Next, we compared the procedures and steps that were taken in each of the cases and derived a model that describes a generic approach how to utilize and process data in order to gain higher level insights. Our model structures the steps from data to knowledge over different levels of complexity and abstraction, namely operational, tactical, and strategic. Thereby, the knowledge acquired in each phase serves as input in the next phase which increases the measurable level of complexity with each iteration. Since the steps in our model are specifically arranged as a pipeline, it enables practitioners to automate a continuous and quantitative measurement of high level insights in real-time.
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| 5. |
- Figalist, Iris, et al.
(författare)
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Customer churn prediction in B2B contexts
- 2019
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Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 370, s. 378-386, s. 378-386
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Konferensbidrag (refereegranskat)abstract
- While business-to-customer (B2C) companies, in the telecom sector for instance, have been making use of customer churn prediction for many years, churn prediction in the business-to-business (B2B) domain receives much less attention in existing literature. Nevertheless, B2B-specific characteristics, such as a lower number of customers with much higher transactional values, indicate the importance of identifying potentially churning customers. To achieve this, we implemented a prediction model for customer churn within a B2B software product and derived a model based on the results. For one, we present an approach that enables the mapping of customer- and end-user-data based on “customer phases” which allows the prediction model to take all critical influencing factors into consideration. In addition to that, we introduce a B2B customer churn prediction process based on the proposed data mapping.
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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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Mining customer satisfaction on b2b online platforms using service quality and web usage metrics
- 2020
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE. - 1530-1362. ; 2020-December, s. 435-444, s. 435-444
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Konferensbidrag (refereegranskat)abstract
- In order to distinguish themselves from their competitors, software service providers constantly try to assess and improve customer satisfaction. However, measuring customer satisfaction in a continuous way is often time and cost intensive, or requires effort on the customer side. Especially in B2B contexts, a continuous assessment of customer satisfaction is difficult to achieve due to potential restrictions and complex provider-customer-end user setups. While concepts such as web usage mining enable software providers to get a deep understanding of how their products are used, its application to quantitatively measure customer satisfaction has not yet been studied in greater detail. For that reason, our study aims at combining existing knowledge on customer satisfaction, web usage mining, and B2B service characteristics to derive a model that enables an automated calculation of quantitative customer satisfaction scores. We apply web usage mining to validate these scores and to compare the usage behavior of satisfied and dissatisfied customers. This approach is based on domain-specific service quality and web usage metrics and is, therefore, suitable for continuous measurements without requiring active customer participation. The applicability of the model is validated by instantiating it in a real-world B2B online platform.
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| 8. |
- 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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| 9. |
- Gerostathopoulos, Ilias, et al.
(författare)
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Continuous Data-driven Software Engineering : Towards a Research Agenda
- 2019
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Ingår i: Software Engineering Notes. - : Association for Computing Machinery (ACM). - 0163-5948 .- 1943-5843. ; 44:3, s. 60-64
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Tidskriftsartikel (refereegranskat)abstract
- The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.
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