Time to Say 'Good Bye': Feature Lifecycle

Fabijan, Aleksander; Olsson, Helena Holmström; Bosch, Jan

doi:10.1109/seaa.2016.59

Cited by 26 publications

(6 citation statements)

References 29 publications

(53 reference statements)

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“…The 'Experimentation Evolution Model' can be used to compare other companies and advise them on what to focus on next in order to efficiently scale their data-driven practices. The embedded systems domain is one example area where companies are aiming to become data-driven and that we previously studied [34], [11], [35]. The phases of our model can be applied to this domain.…”

Section: Validity Considerations 1) Construct Validitymentioning

confidence: 99%

The Evolution of Continuous Experimentation in Software Product Development: From Data to a Data-Driven Organization at Scale

Fabijan

Dmitriev

Olsson

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

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114

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Abstract-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. A/B

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Section: Validity Considerations 1) Construct Validitymentioning

confidence: 99%

The Evolution of Continuous Experimentation in Software Product Development: From Data to a Data-Driven Organization at Scale

Fabijan

Dmitriev

Olsson

et al. 2017

2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)

Self Cite

114

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“…28 Furthermore, CD increases the system's complexity as a consequence of more features being introduced in the code leading to more sophisticated interactions between the features, even if not used. 57 This increased complexity makes postdeployment activities such as troubleshooting, fault identification, and root cause analysis more difficult. Therefore, to succeed in transitioning to CD, it is critical to establish a dedicated support team equipped with intelligent tools to perform continuous monitoring of live systems.…”

Section: Product-related Services Challenges With CDmentioning

confidence: 99%

“…79 In addition, CD further increases the complexity of the software as a consequence of increased complexity in features interactions. 57 Therefore, delivering services by relying on human force only becomes challenging. Thus, as depicted in Figure 9, AIOps become critical to break the complexity curve while delivering faster and more intelligent services.…”

Section: Aiops and CDmentioning

confidence: 99%

“…Furthermore, CD increases the system's complexity as a consequence of more features being introduced in the code leading to more sophisticated interactions between the features, even if not used 57 . This increased complexity makes postdeployment activities such as troubleshooting, fault identification, and root cause analysis more difficult.…”

Section: Introductionmentioning

confidence: 99%

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Towards AIOps enabled services in continuously evolving software‐intensive embedded systems

Dakkak

Bosch

Olsson

2023

J Software Evolu Process

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Continuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time‐consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software‐intensive embedded systems often provide customer product‐related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in‐depth understanding of the impact of continuous deployment on product‐related services provided by software‐intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software‐intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product‐related services become blurry with continuous deployment. In addition, product‐related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software‐intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement.

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“…To identify metrics that are key to the business can be challenging [44], [233]. It can be intuitive to measure and consider short-term effect in OEC metrics [184].…”

Section: Metrics and Valuementioning

confidence: 99%

Data-Driven Software Development at Large Scale : from Ad-Hoc Data Collection to Trustworthy Experimentation

Fabijan¹

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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 threefold. 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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Time to Say 'Good Bye': Feature Lifecycle

Cited by 26 publications

References 29 publications

The Evolution of Continuous Experimentation in Software Product Development: From Data to a Data-Driven Organization at Scale

The Evolution of Continuous Experimentation in Software Product Development: From Data to a Data-Driven Organization at Scale

Towards AIOps enabled services in continuously evolving software‐intensive embedded systems

Data-Driven Software Development at Large Scale : from Ad-Hoc Data Collection to Trustworthy Experimentation

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