Following the successful completion of the DIMECC N4S program, a publicly available N4S Treasure Chest has been released.

See our story in the narratives section in there.

We have recently completed successfully the DD-SCALE (Distributed dynamic software development work in global value networks – framework, tools and work expertise practices) joint-project with the University of Tampere and Haaga-Helia University of Applied Sciences as research partners and ABB, Comptel, Napa and Nokia as the industrial partners. The Tekes-funded project period was 9/2014-9/2016 with a closing seminar in February 2017.

Productivity in software-intensive product and service development has been a persistent research challenge for decades. Considering total productivity, it is essential to understand holistically the role of software and their development in organizations. In practice, it is not possible to explain conclusively all the business impacts of software development related decisions. Furthermore, the net customer value provided by software is influenced by many company external, non-controllable factors.

However, key factors affecting the total productivity are knowledge and competencies coupled with the ability of the company to leverage them. The company can influence those with various decisions and activities both positively and – possibly unintentionally – negatively. In terms of total productivity, it is imperative to understand that even single, determined decisions and the roles of certain individuals may have major impacts of the performance of the entire organization (e.g., software architectural solutions). In our DD-SCALE research work we have shed light on such factors and events in our industry partner cases. Resulting research publications are currently in preparation.

Further reading (in Finnish): DD-SCALE -tutkimusprojektin päätösseminaari

Our university is going to start a new spearhead organization integration: HLSC

While that is not explicitly about computer and software sciences, it is obvious that software will have at least an enabler role in there. Indeed, what everything could empirical software engineering contribute there?

Consider for instance something like this: Integration platform for biomedical image analysis

Now that even the Finnish government is striving for productivity leaps and cost-efficiencies with digitalization, it is increasingly important to understand, what exactly those general terms mean in software engineering and which factors influence them. Moreover, empirical evidence is needed to justify the underlying assumptions (e.g., working hours vs. value-add).