| 1. |
- Chrobak, Marek, et al.
(author)
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Online Clique Clustering
- 2019
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In: Algorithmica. - : Springer. - 0178-4617 .- 1432-0541. ; 82:4, s. 938-965
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Journal article (peer-reviewed)abstract
- Clique clustering is the problem of partitioning the vertices of a graph into disjoint clusters, where each cluster forms a clique in the graph, while optimizing some objective function. In online clustering, the input graph is given one vertex at a time, and any vertices that have previously been clustered together are not allowed to be separated. The goal is to maintain a clustering with an objective value close to the optimal solution. For the variant where we want to maximize the number of edges in the clusters, we propose an online algorithm based on the doubling technique. It has an asymptotic competitive ratio at most 15.646 and a strict competitive ratio at most 22.641. We also show that no deterministic algorithm can have an asymptotic competitive ratio better than 6. For the variant where we want to minimize the number of edges between clusters, we show that the deterministic competitive ratio of the problem is n−ω(1), where n is the number of vertices in the graph.
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| 2. |
- Fabijan, Aleksander, et al.
(author)
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Commodity eats innovation for breakfast: A model for differentiating feature realization
- 2016
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In: 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. - 9783319490939 ; 10027 LNCS, s. 517-525, s. 517-525
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Conference paper (peer-reviewed)abstract
- Once supporting the electrical and mechanical functionality, software today became the main competitive advantage in products. However, in the companies that we study, the way in which software features are developed still reflects the traditional ‘requirements over the wall’ approach. As a consequence, individual departments prioritize what they believe is the most important and are unable to identify which features are regularly used – ‘flow’, there to be bought – ‘wow’, differentiating and that add value to customers, or which are regarded commodity. In this paper, and based on case study research in three large software-intensive companies, we (1) provide empirical evidence that companies do not distinguish between different types of features, which causes poor allocation of R&D efforts and suppresses innovation, and (2) develop a model in which we depict the activities for differentiating and working with different types of features and stakeholders.
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| 3. |
- Fabijan, Aleksander, et al.
(author)
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Competitive Online Clique Clustering
- 2013
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In: Proceedings of the 8th International Conference on Algorithms and Complexity;8. - Berlin, Heidelberg : Springer. ; , s. 221-233
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Conference paper (peer-reviewed)abstract
- Clique clustering is the problem of partitioning a graph into cliques so that some objective function is optimized. In online clustering, the input graph is given one vertex at a time, and any vertices that have previously been clustered together are not allowed to be separated. The objective here is to maintain a clustering the never deviates too far in the objective function compared to the optimal solution. We give a constant competitive upper bound for online clique clustering, where the objective function is to maximize the number of edges inside the clusters. We also give almost matching upper and lower bounds on the competitive ratio for online clique clustering, where we want to minimize the number of edges between clusters. In addition, we prove that the greedy method only gives linear competitive ratio for these problems.
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| 4. |
- Fabijan, Aleksander, et al.
(author)
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Customer Feedback and Data Collection Techniques : A Systematic Literature Review on the Role and Impact of Feedback in Software Product Development
- 2016
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Other publication (other academic/artistic)abstract
- Background: Customer feedback is critical for successful product development. Software companies continuously collect it in order to become more data-driven. By understanding how these feedback data are collected, companies’ ability to accumulate and synthesize the learnings, and correctly prioritize product development decisions increases. Objective: The purpose of this study is to (1) provide an overview of the sources and feedback collection techniques, (2) demonstrate the impact that customer and product data have on product development, and (3) provide the open research challenges on this topic. Method: We performed a systematic literature review of customer feedback and data collection techniques, analyzing 71 papers on the subject taken from a gross collection of 1298. Results: We (1) identify the different customer feedback techniques and sources where these data originate and summarize them in the “Customer Feedback Model”. Next, we show the (2) impact that the customer feedback has on the overall development process. Finally, we (3) conclude with future research challenges. Conclusions: Our research reveals a compelling set of feedback data collection techniques that can be used throughout the development stages of software products. The identified challenges, however, indicate that the use of feedback today is fragmented and with limited tool support.
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| 5. |
- Fabijan, Aleksander
(author)
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Data-Driven Software Development at Large Scale : from Ad-Hoc Data Collection to Trustworthy Experimentation
- 2018
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Doctoral thesis (other academic/artistic)abstract
- Accurately learning what customers value is critical for the success of every company. Despite the extensive research on identifying customer preferences, only a handful of software companies succeed in becoming truly data-driven at scale. Benefiting from novel approaches such as experimentation in addition to the traditional feedback collection is challenging, yet tremendously impactful when performed correctly. In this thesis, we explore how software companies evolve from data-collectors with ad-hoc benefits, to trustworthy data-driven decision makers at scale. We base our work on a 3.5-year longitudinal multiple-case study research with companies working in both embedded systems domain (e.g. engineering connected vehicles, surveillance systems, etc.) as well as in the online domain (e.g. developing search engines, mobile applications, etc.). The contribution of this thesis is three-fold. First, we present how software companies use data to learn from customers. Second, we show how to adopt and evolve controlled experimentation to become more accurate in learning what customers value. Finally, we provide detailed guidelines that can be used by companies to improve their experimentation capabilities. With our work, we aim to empower software companies to become truly data-driven at scale through trustworthy experimentation. Ultimately this should lead to better software products and services.
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| 6. |
- Fabijan, Aleksander
(author)
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Developing the right features : the role and impact of customer and product data in software product development
- 2016
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Licentiate thesis (other academic/artistic)abstract
- Software product development companies are increasingly striving to become data-driven. The access to customer feedback and product data has been, with products increasingly becoming connected to the Internet, demonetized. Systematically collecting the feedback and efficiently using it in product development, however, are challenges that large-scale software development companies face today when being faced by large amounts of available data. In this thesis, we explore the collection, use and impact of customer feedback on software product development. We base our work on a 2-year longitudinal multiple-case study research with case companies in the software-intensive domain, and complement it with a systematic review of the literature. In our work, we identify and confirm that large-software companies today collect vast amounts of feedback data, however, struggle to effectively use it. And due to this situation, there is a risk of prioritizing the development of features that may not deliver value to customers. Our contribution to this problem is threefold. First, we present a comprehensive and systematic review of activities and techniques used to collect customer feedback and product data in software product development. Next, we show that the impact of customer feedback evolves over time, but due to the lack of sharing of the collected data, companies do not fully benefit from this feedback. Finally, we provide an improvement framework for practitioners and researchers to use the collected feedback data in order to differentiate between different feature types and to model feature value during the lifecycle. With our contributions, we aim to bring software companies one step closer to data-driven decision making in software product development.
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| 7. |
- Fabijan, Aleksander, et al.
(author)
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Differentiating Feature Realization in Software Product Development
- 2017
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In: Product-Focused Software Process Improvement. - Cham : Springer. ; 10611 LNCS, s. 221-236
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Conference paper (peer-reviewed)abstract
- Software is no longer only supporting mechanical and electrical products. Today, it is becoming the main competitive advantage and an enabler of innovation. Not all software, however, has an equal impact on customers. Companies still struggle to differentiate between the features that are regularly used, there to be for sale, differentiating and that add value to customers, or which are regarded commodity. Goal: The aim of this paper is to (1) identify the different types of software features that we can find in software products today, and (2) recommend how to prioritize the development activities for each of them. Method: In this paper, we conduct a case study with five large-scale software intensive companies. Results: Our main result is a model in which we differentiate between four fundamentally different types of features (e.g. ‘Checkbox’, ‘Flow’, ‘Duty’ and ‘Wow’). Conclusions: Our model helps companies in (1) differentiating between the feature types, and (2) selecting an optimal methodology for their development (e.g. ‘Output-Driven’ vs. ‘Outcome-Driven’).
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| 8. |
- Fabijan, Aleksander, et al.
(author)
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Early value argumentation and prediction: An iterative approach to quantifying feature value
- 2015
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In: 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. - 9783319268439 ; 9459, s. 16-23, s. 16-23
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Conference paper (peer-reviewed)abstract
- Companies are continuously improving their practices and ways of working in order to fulfill always-changing market requirements. As an example of building a better understanding of their customers, organizations are collecting user feedback and trying to direct their R&D efforts by e.g. continuing to develop features that deliver value to the customer. We (1) develop an actionable technique that practitioners in organizations can use to validate feature value early in the development cycle, (2) validate if and when the expected value reflects on the customers, (3) know when to stop developing it, and (4) identity unexpected business value early during development and redirect R&D effort to capture this value. The technique has been validated in three experiments in two cases companies. Our findings show that predicting value for features under development helps product management in large organizations to correctly re-prioritize R&D investments.
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| 9. |
- Fabijan, Aleksander, et al.
(author)
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Effective Online Controlled Experiment Analysis at Large Scale
- 2018
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In: Proceedings of the EUROMICRO Conference. - : IEEE. ; , s. 64-67
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Conference paper (peer-reviewed)abstract
- Online Controlled Experiments (OCEs) are the norm in data-driven software companies because of the benefits they provide for building and deploying software. Product teams experiment to accurately learn whether the changes that they do to their products (e.g. adding new features) cause any impact (e.g. customers use them more frequently). Experiments also help reduce the risk from deploying software by minimizing the magnitude and duration of harm caused by software bugs, allowing software to be shipped more frequently. To make informed decisions in product development, experiment analysis needs to be granular with a large number of metrics over heterogeneous devices and audiences. Discovering experiment insights by hand, however, can be cumbersome. In this paper, and based on case study research at a large-scale software development company with a long tradition of experimentation, we (1) describe the standard process of experiment analysis, and (2) introduce an artifact to improve the effectiveness and comprehensiveness of this process.
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| 10. |
- Fabijan, Aleksander, et al.
(author)
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Experimentation growth: Evolving trustworthy A/B testing capabilities in online software companies
- 2018
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In: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 30:12
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Journal article (peer-reviewed)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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