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  • 1.
    Chrobak, Marek
    et al.
    Department of Computer Science and Engineering, University of California at Riverside, Riverside, USA.
    Dürr, Christoph
    Laboratoire d’informatique de Paris 6, LIP6, CNRS, Sorbonne Université, 75252, Paris, France.
    Fabijan, Aleksander
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Nilsson, Bengt J.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Online Clique Clustering2019Ingår i: Algorithmica, ISSN 0178-4617, E-ISSN 1432-0541, Vol. 82, nr 4, s. 938-965Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Data-Driven Software Development at Large Scale: from Ad-Hoc Data Collection to Trustworthy Experimentation2018Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    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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  • 3.
    Fabijan, Aleksander
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Developing the right features: the role and impact of customer and product data in software product development2016Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    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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  • 4.
    Fabijan, Aleksander
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dmitriev, Pavel
    Outreach.io, Seattle, WA, USA.
    McFarland, Colin
    Skyscanner, Edinburgh, Scotland, UK.
    Vermeer, Lukas
    BOOKING.COM, Amsterdam, Netherlands.
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University, Gothenburg, Sweden.
    Experimentation growth: Evolving trustworthy A/B testing capabilities in online software companies2018Ingår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 30, nr 12, artikel-id e2113Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 5.
    Fabijan, Aleksander
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dmitriev, Pavel
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Effective Online Controlled Experiment Analysis at Large Scale2018Ingår i: Proceedings of the EUROMICRO Conference, IEEE, 2018, s. 64-67Konferensbidrag (Refereegranskat)
    Abstract [en]

    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.

  • 6.
    Fabijan, Aleksander
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dmitriev, Pavel
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Online Controlled Experimentation at Scale: An Empirical Survey on the Current State of A/B Testing2018Ingår i: Proceedings of the EUROMICRO Conference, IEEE, 2018, s. 68-72Konferensbidrag (Refereegranskat)
    Abstract [en]

    Online Controlled Experiments (OCEs, aka A/B tests) are one of the most powerful methods for measuring how much value new features and changes deployed to software products bring to users. Companies like Microsoft, Amazon, and Booking.com report the ability to conduct thousands of OCEs every year. However, the competences of the remainder of the online software industry remain unknown. The main objective of this paper is to reveal the current state of A/B testing maturity in the software industry based on a maturity model from our previous research. We base our findings on 44 responses from an online empirical survey. Our main contribution of this paper is the current state of experimentation maturity as operationalized by the ExG model for a convenience sample of companies doing online controlled experiments. Our findings show that, among others, companies typically develop in-house experimentation platforms, that these platforms are of various levels of maturity, and that designing key metrics - Overall Evaluation Criteria - remains the key challenge for successful experimentation.

  • 7.
    Fabijan, Aleksander
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dmitriev, Pavel
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Vermeer, Lukas
    Lewis, Dylan
    Three Key Checklists and Remedies for Trustworthy Analysis of Online Controlled Experiments at Scale2019Ingår i: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2019), IEEE, 2019, s. 1-10Konferensbidrag (Refereegranskat)
    Abstract [en]

    Online Controlled Experiments (OCEs) are transforming the decision-making process of data-driven companies into an experimental laboratory. Despite their great power in identifying what customers actually value, experimentation is very sensitive to data loss, skipped checks, wrong designs, and many other 'hiccups' in the analysis process. For this purpose, experiment analysis has traditionally been done by experienced data analysts and scientists that closely monitored experiments throughout their lifecycle. Depending solely on scarce experts, however, is neither scalable nor bulletproof. To democratize experimentation, analysis should be streamlined and meticulously performed by engineers, managers, or others responsible for the development of a product. In this paper, based on synthesized experience of companies that run thousands of OCEs per year, we examined how experts inspect online experiments. We reveal that most of the experiment analysis happens before OCEs are even started, and we summarize the key analysis steps in three checklists. The value of the checklists is threefold. First, they can increase the accuracy of experiment setup and decision-making process. Second, checklists can enable novice data scientists and software engineers to become more autonomous in setting-up and analyzing experiments. Finally, they can serve as a base to develop trustworthy platforms and tools for OCE set-up and analysis.

  • 8.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dmitriev, Pavel
    Microsoft Analysis & Experimentation, Redmond, USA.
    Olsson, Helena Holmström
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Dep. of Computer Science, Chalmers University of Tech., Göteborg, Sweden.
    Bosh, Jan
    The Benefits of Controlled Experimentation at Scale2017Ingår i: 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), IEEE, 2017, s. 18-26Konferensbidrag (Refereegranskat)
    Abstract [en]

    Online controlled experiments (for example A/B tests) are increasingly being performed to guide product development and accelerate innovation in online software product companies. The benefits of controlled experiments have been shown in many cases with incremental product improvement as the objective. In this paper, we demonstrate that the value of controlled experimentation at scale extends beyond this recognized scenario. Based on an exhaustive and collaborative case study in a large software-intensive company with highly developed experimentation culture, we inductively derive the benefits of controlled experimentation. The contribution of our paper is twofold. First, we present a comprehensive list of benefits and illustrate our findings with five case examples of controlled experiments conducted at Microsoft. Second, we provide guidance on how to achieve each of the benefits. With our work, we aim to provide practitioners in the online domain with knowledge on how to use controlled experimentation to maximize the benefits on the portfolio, product and team level.

  • 9.
    Fabijan, Aleksander
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dmitriev, Pavel
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    The Online Controlled Experiment Lifecycle2020Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 37, nr 2, s. 60-67Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Online Controlled Experiments (OCEs) enable an accurate understanding of customer value and generate millions of dollars of additional revenue at Microsoft. Unlike other techniques for learning from customers, OCEs establish an accurate and causal relationship between a change and the impact observed. Although previous research describes technical and statistical dimensions, the key phases of online experimentation are not widely known, their impact and importance are obscure, and how to establish OCEs in an organization is underexplored. In this paper, using a longitudinal in-depth case study, we address this gap by (1) presenting the Experiment Lifecycle, and (2) demonstrating with four example experiments their profound impact. We show that OECs help optimize infrastructure needs and aid in project planning and measuring team efforts, in addition to their primary goal of accurately identifying what customers value. We conclude that product development should fully integrate the Experiment Lifecycle to benefit from the OCEs.

  • 10.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Dmitriev, Pavel
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosh, Jan
    The Evolution of Continuous Experimentation in Software Product Development: From Data to a Data-Driven Organization at Scale2017Ingår i: International Conference on Software Engineering. Proceedings, IEEE, 2017, s. 770-780Konferensbidrag (Refereegranskat)
    Abstract [en]

    Software development companies are increasingly aiming to become data-driven by trying to continuously experiment with the products used by their customers. Although familiar with the competitive edge that the A/B testing technology delivers, they seldom succeed in evolving and adopting the methodology. In this paper, and based on an exhaustive and collaborative case study research in a large software-intense company with highly developed experimentation culture, we present the evolution process of moving from ad-hoc customer data analysis towards continuous controlled experimentation at scale. Our main contribution is the "Experimentation Evolution Model" in which we detail three phases of evolution: technical, organizational and business evolution. With our contribution, we aim to provide guidance to practitioners on how to develop and scale continuous experimentation in software organizations with the purpose of becoming data-driven at scale.

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  • 11.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Holmström Olsson, H.
    Bosch, J.
    Customer Feedback and Data Collection Techniques: A Systematic Literature Review on the Role and Impact of Feedback in Software Product Development2016Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    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. 

  • 12.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Teknik och samhälle (TS).
    Nilsson, Bengt J.
    Malmö högskola, Teknik och samhälle (TS).
    Persson, Mia
    Malmö högskola, Teknik och samhälle (TS).
    Competitive Online Clique Clustering2013Ingår i: Proceedings of the 8th International Conference on Algorithms and Complexity;8, Springer, 2013, s. 221-233Konferensbidrag (Refereegranskat)
    Abstract [en]

    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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  • 13.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosch, Jan
    Commodity Eats Innovation for Breakfast: A Model for Differentiating Feature Realization2016Ingår i: Product-Focused Software Process Improvement: 17th International Conference, PROFES 2016, Trondheim, Norway, November 22-24, 2016, Proceedings, Springer, 2016, s. 517-525Konferensbidrag (Refereegranskat)
    Abstract [en]

    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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  • 14.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Bosch, Jan
    Customer Feedback and Data Collection Techniques in Software R&D: A Literature Review2015Ingår i: Software Business: 6th International Conference, ICSOB 2015, Braga, Portugal, June 10-12, 2015, Proceedings, Springer, 2015, s. 139-153Konferensbidrag (Refereegranskat)
    Abstract [en]

    In many companies, product management struggles in getting accurate customer feedback. Often, validation and confirmation of functionality with customers takes place only after the product has been deployed, and there are no mechanisms that help product managers to continuously learn from customers. Although there are techniques available for collecting customer feedback, these are typically not applied as part of a continuous feedback loop. As a result, the selection and prioritization of features becomes far from optimal, and product deviates from what the customers need. In this paper, we present a literature review of currently recognized techniques for collecting customer feedback. We develop a model in which we categorize the techniques according to their characteristics. The purpose of this literature review is to provide an overview of current software engineering research in this area and to better understand the different techniques that are used for collecting customer feedback.

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  • 15.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosch, Jan
    Data-Driven Decision-Making in Product R&D2015Ingår i: Agile Processes in Software Engineering and Extreme Programming: 16th International Conference, XP 2015, Helsinki, Finland, May 25-29, 2015, Proceedings, Springer, 2015, s. 350-351Konferensbidrag (Refereegranskat)
    Abstract [en]

    Software development companies experience the road mapping and requirements ranking process to be complex as product management (PdM) strives in getting timely and accurate feedback from the customers. Often, companies have insufficient knowledge about how their products are being used, what features the customers appreciate and which ones will generate revenue. To address this problem, this research aims at helping the companies in closing the 'open' feedback loop that exists between PdM and customers. Moreover, the research strives at exploring techniques that can be used to involve customers in continuous validation of software functionality in order to provide PdM with the evidence needed for accurate R&D investments.

  • 16.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Bosch, Jan
    Early Value Argumentation and Prediction: An Iterative Approach to Quantifying Feature Value2015Ingår i: Product-Focused Software Process Improvement, Springer, 2015, s. 16-23Konferensbidrag (Refereegranskat)
    Abstract [en]

    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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  • 17.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosch, Jan
    The Lack of Sharing of Customer Data in Large Software Organizations: Challenges and Implications2016Ingår i: Agile Processes, in Software Engineering, and Extreme Programming, Springer, 2016, s. 39-52Konferensbidrag (Refereegranskat)
    Abstract [en]

    With agile teams becoming increasingly multi-disciplinary and including all functions, the role of customer feedback is gaining momentum. Today, companies collect feedback directly from customers, as well as indirectly from their products. As a result, companies face a situation in which the amount of data from which they can learn about their customers is larger than ever before. In previous studies, the collection of data is often identified as challenging. However, and as illustrated in our research, the challenge is not the collection of data but rather how to share this data among people in order to make effective use of it. In this paper, and based on case study research in three large software-intensive companies, we (1) provide empirical evidence that ‘lack of sharing’ is the primary reason for insufficient use of customer and product data, and (2) develop a model in which we identify what data is collected, by whom data is collected and in what development phases it is used. In particular, the model depicts critical hand-overs where certain types of data get lost, as well as the implications associated with this. We conclude that companies benefit from a very limited part of the data they collect, and that lack of sharing of data drives inaccurate assumptions of what constitutes customer value.

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  • 18.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosch, Jan
    Time to Say 'Good Bye': Feature Lifecycle2016Ingår i: Proceedings 42nd Euromicro Conference on Software Engineering and Advanced Applications SEAA 2016, IEEE, 2016, s. 9-16Konferensbidrag (Refereegranskat)
    Abstract [en]

    With continuous deployment of software functionality, a constant flow of new features to products is enabled. Although new functionality has potential to deliver improvements and possibilities that were previously not available, it does not necessary generate business value. On the contrary, with fast and increasing system complexity that is associated with high operational costs, more waste than value risks to be created. Validating how much value a feature actually delivers, project how this value will change over time, and know when to remove the feature from the product are the challenges large software companies increasingly experience today. We propose and study the concept of a software feature lifecycle from a value point of view, i.e. how companies track feature value throughout the feature lifecycle. The contribution of this paper is a model that illustrates how to determine (1) when to add the feature to a product, (2) how to track and (3) project the value of the feature during the lifecycle, and how to (4) identify when a feature is obsolete and should be removed from the product.

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  • 19.
    Fabijan, Aleksander
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosh, Jan
    Differentiating Feature Realization in Software Product Development2017Ingår i: Product-Focused Software Process Improvement: Product-Focused Software Process Improvement. PROFES 2017., Springer, 2017, s. 221-236Konferensbidrag (Refereegranskat)
    Abstract [en]

    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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  • 20. Gupta, Somit
    et al.
    Ulanova, Lucy
    Bhardwaj, Sumit
    Dmitriev, Pavel
    Raff, Paul
    Fabijan, Aleksander
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    The Anatomy of a Large-Scale Experimentation Platform2018Ingår i: 2018 IEEE International Conference on Software Architecture (ICSA), IEEE, 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Online controlled experiments (e.g., A/B tests) are an integral part of successful data-driven companies. At Microsoft, supporting experimentation poses a unique challenge due to the wide variety of products being developed, along with the fact that experimentation capabilities had to be added to existing, mature products with codebases that go back decades. This paper describes the Microsoft ExP Platform (ExP for short) which enables trustworthy A/B experimentation at scale for products across Microsoft, from web properties (such as bing.com) to mobile apps to device drivers within the Windows operating system. The two core tenets of the platform are trustworthiness (an experiment is meaningful only if its results can be trusted) and scalability (we aspire to expose every single change in any product through an A/B experiment). Currently, over ten thousand experiments are run annually. In this paper, we describe the four core components of an A/B experimentation system: experimentation portal, experiment execution service, log processing service and analysis service, and explain the reasoning behind the design choices made. These four components work together to provide a system where ideas can turn into experiments within minutes and those experiments can provide initial trustworthy results within hours.

  • 21. Mattos Issa, David
    et al.
    Dimitriev, Pavel
    Fabijan, Aleksander
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    An Activity and Metric Model for Online Controlled Experiments2018Ingår i: PROFES 2018: Product-Focused Software Process Improvement, Springer, 2018, s. 182-198Konferensbidrag (Refereegranskat)
    Abstract [en]

    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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    FULLTEXT01
  • 22.
    Olsson Holmström, Helena
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosch, Jan
    Fabijan, Aleksander
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Experimentation that Matters: A Multi-case Study on the Challenges with A/B Testing2017Ingår i: Software Business: 8th International Conference, ICSOB 2017, Essen, Germany, June 12-13, 2017, Proceedings, Springer, 2017, s. 179-185Konferensbidrag (Refereegranskat)
    Abstract [en]

    From having been exclusive for companies in the online domain, feature experiments are becoming increasingly important for software-intensive companies also in other domains. Today, companies run experiments, such as e.g. A/B tests, to optimize product performance and to learn about user behaviors, as well as to guide product development and innovation. However, although experimentation with customers has become an effective mechanism to improve products and increase revenue, companies struggle with how to leverage the results of the experiments they run. In this paper, we study the reasons for this and we identify three key challenges that make feature experimentation a difficult task. Our research reveals the following challenges: (1) the impact of experiments doesn't scale, (2) business KPIs and team level metrics are not aligned and (3) it is unclear if the available solutions are applicable across domains.

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