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Optimizing and Redesigning the Complex Software Development Process with AI

Michael Ihringer
Nov 30, 2020 5:15:00 PM

Interview with Prof. Dr. Jürgen Döllner, Head of the AI Lab at Hasso Plattner Institute

Published in “AI Trendletter” (in German). Translated in March 2021 by Brandon Lewis.

Optimizing and redesigning the complex software manufacturing process with AI.


The Hasso Plattner Institute for Digital Engineering (HPI) is researching the application of artificial intelligence (AI) in software engineering at the new AI Lab for Software Engineering. The goal is to identify those AI approaches for the management of complex software projects with which the greatest possible efficiency potentials can be realized.

Prof. Dr. Jürgen Döllner, the head of the AI Lab, explains the background in an interview. He also describes the goals of the planned HPI study "AI for Software Engineering", in which interested companies that want to learn about the potential of AI for their own software engineering can participate. The submitted software development projects should have been running for at least two years and involve ten or more developers. The selected companies will receive a quick introduction to the future topic of "AI in software engineering" with guidelines for setting up their own AI strategy, an analysis and evaluation of software development based on the submitted project, and benchmarking within the field of participants.

Prof. Döllner, AI is one of the hot topics of our time. Why has AI not yet been used in software engineering, of all places?

Prof. Dr. Jürgen Döllner: The first practical applications of AI were in optical character recognition – to recognize handwritten addresses, for example. In many applications, however, AI could not fully develop its effect because the performance of the hardware was not yet sufficient or the data was not of sufficient quality. Ultimately, AI relies on high computing power and Big Data. Without meaningful training data, for example, AI cannot make sufficiently accurate decisions. That's a big reason why machine learning didn't spread immediately.

In software engineering itself, AI has hardly been applied so far. There has never been a real paradigm shift here: Even in the "agile age", software production is still more like an artisan craft than an industrial process with plannable, calculable results. 

Today, however, there is plenty of data that could be analyzed using Big Data methods. Each individual software tool brings its own repository with it.

It is indeed a peculiar situation: In repositories and all the other tools, we have excellent, precise data about all essential aspects of software development. We know at any point in time who changed which code on which module. We also know when and where there was a bug and who handled it.

The complexity of the software development process 

And with this paradigm shift, study participants then enjoy the benefit of early entry?

Exactly – they have the advantage of the early use of this technology to make their own software development competitive and fit for the future. This paradigm shift will play a major role in determining whether companies can be successful with their software development in the future.

How can you participate?

The study starts in January, until then we are accepting project proposals. We then discuss with the applicants whether their project is suitable - it should have a certain minimum size and have already reached a certain runtime. Then we ask which software repositories will be used to ensure that we can implement the connection to our analysis systems. If everything fits, we observe, interpret and analyze the software development processes with AI-based methods from January. At the end, we evaluate and improve them.

Prof. Döllner, thank you very much for the interview!

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