Eternal Embedded Software: Towards Innovation Experiment Systems

Industrial Challenges of Scaling Agile in Mass-Produced Embedded Systems

“…Moreover, while the ability to manufacture high-quality mechanical systems is still critical, it is no longer the only differentiator and what makes a company competitive. During the last two decades, electronics and software have been introduced into many products, and embedded systems companies are becoming increasingly software-intensive with software being the key differentiator [1]. This requires a significant shift in the ways-of-working within these companies, and currently many large companies within the embedded systems domain struggle with the alignment of hardware and software development cycles and practices [1].…”

Hitting the Target: Practices for Moving Toward Innovation Experiment Systems

“…The adoption of these practices reflects an evolution in which companies move beyond agile practices towards R&D practices characterised by short release cycles, frequent customer validation and fully automated testing and deployment practices. Although the same agile R&D principles apply, moving beyond agile practices means: a) integrating business strategy planning, operations and other corporate functions into shorter development and release cycles [4], [15]; b) utilising automated testing practices that allow for frequent builds [12] and c) implementing continuous experimentation and innovation with customers [2,3,4] to better understand real customer needs. The specific aspects involved in going beyond agile as well as more holistic views of agility have been discussed in recent SE studies [15,16] and especially in the context of lean software development [17].…”

“…In step E, companies adopt data collection mechanisms to continuously learn about customer behaviour and product use. Feature experiments are run on a continuous basis and the collected data steer the R&D organisation [2,3]. Rather than being specified by the product management in the early phase of development, requirements evolve based on data collected from real-time customer use.…”

Early Value Argumentation and Prediction: An Iterative Approach to Quantifying Feature Value

“…Organizations are struggling to know if and how much of the feature that they are developing, will actually deliver value to their customers. To overcome this problem, and to be able to predict and validate feature value, companies have to continuously be able to collect customer feedback and learn from the users [6], [10]. The QCD model, due to its nature of being very dynamic when forming hypotheses and able to handle both quantitative as well as qualitative data [4], is the current 'state of the art' instrument for continuous experimentation.…”

“…Learning and validating with users of the products is becoming increasingly important and organizations need to adapt their processes in order to take this source of feature validation into account [7], [8]. First seen in the B2C domain and recently also in B2B domain [6], products are becoming connected and by having data collection techniques in place, numerous new options are available to use this information.…”