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The right team and its organization is THE critical success factor of any software project, which is why significant resources are invested in this area. Nevertheless, some IT managers would prefer to deal directly with the actual project content. A research team at the Hasso Plattner Institute (HPI) in Potsdam is already working intensively on a solution that should interest all IT experts: An AI assistant, fed a large amount of relevant data, that plans entire IT projects for managers and presents optimized team structures, schedules, and effort forecasts. HPI is taking a significant step towards its vision with a recent research project. We at Seerene are happy to help and to be able to give a first glimpse of the project here.
Every practitioner of modern software production is painfully aware that managing a larger team of developers involves not only technical challenges but also very demanding logistical problems. For example, the question of which developers best master certain tasks, how teams should be assembled, or which inputs and processes determine the required project time. This basic information is not easy to compile, even in a clearly structured and heterogeneous team. With teams of more than 30 or even hundreds of variably specialized developers, however, it already requires a disproportionate amount of time and effort. The efficiency losses that can result from poorly forecasted outlays, unclear technical challenges, mismatched special skills of individual developers, or non-transparent time availability quickly add up to dangerous resource traps. Up to now, IT decision-makers have had few automatic tools at their disposal to assist them with these challenges.
The inefficiency and ever latent danger of "complete logistical failure" of complex software projects are well documented. Many IT experts have therefore treated them as a given law of nature. "Not so fast!" says the software analytics team at HPI in Potsdam. The team, consisting of experienced software developers, Daniel Limberger and Willy Scheibel, as well as Prof. Döllner, has full access to Seerene's powerful software analytics platform, which otherwise only corporations use to remain in command of their gigantic software factories. As such, the project is equipped with the best available resources for the analysis and structured aggregation of entire software projects and indeed all related data traces.
The research project has a very clear objective: to provide managers of large IT units with a software tool that simplifies the configuration of teams and the planning of projects. Proposals, generated on the basis of the best available data, reduce the planning effort from days to just a few seconds, while significantly improving its quality by taking many variables into consideration.
The underlying approach of the research project uses Natural Language Processing as an important pillar. What is astonishing for the layman is that here not only messages, work tickets, and any other digital human communication, but also the programming code is regarded as a linguistic expression and both types of communication are included in the analysis, in a certain way, equally. As a result, the program initially analyzes all data and data traces of past projects up to the present without the bias of the human/machine distinction. On this basis, it creates clusters for the processed tasks on the one hand and profiles for all developers involved on the other. These continuously updated profiles can also be used to create ideal team/schedule configurations for upcoming projects.
So far, so good. But when you learn more about the approach, you quickly realize that the algorithms' approach to compiling performance clusters or developer profiles goes well beyond the ability and understanding of humans. For example, a developer is not simply given a profile whose parameters can be easily described. As such, the values determined are not purely about common characteristics such as perhaps programming language skills, speed, endurance, and complexity management that people would apply. No, there are parameters that are summarized by the AI from a wide variety of dimensions. Their relevance or predictive power aims to be significantly superior to conventional evaluation parameters in terms of speed and stability. How can one imagine this?
A perfect match between the developer and the set of tasks to be completed is created when the analysis applies criteria, each of which contains a large number of different parameters in a situationally ideal weighting. Things like the depth of knowledge or experience in a programming language still play an important role, but not a sole or isolatable role. The properties interact, in some proportion, with many others, which together give the maximum predictive power. Only through this abstraction can the matching make use of the maximum possibilities of data analysis.
While the first AI analyses of real teams and projects using the Seerene Digital Engineering Platform already give cause for great confidence, the research group is currently working on putting the results of their analysis into a form that can be reinterpreted and visualized for the target group of IT executives. Decision-makers must ultimately be able to understand their "superhumanly analytical" digital assistant and its suggestions. Only then can AI lighten the load and optimize practical decisions. "AI uses a mathematical formalism that we cannot interpret directly and we must first understand it ourselves in order to be able to translate it into practical human decisional logic afterwards. In doing so, we can initially interpret the machine-generated suggestions only with the utmost caution" says Atzberger. However, the research group's initial tests make them extremely confident that AI will generate an applicable solution here. "For example, we have been able to determine very specific characteristics of individual developers amazingly well through our system, which have been confirmed to us by the developers themselves and their colleagues. In addition, we have also already been able to uncover interesting correlations that the studied development teams themselves had never noticed before. This is the kind of added value we ultimately want to deliver methodically," says Daniel Atzberger. Indeed, it has already been possible to harmonize some human experiences with the AI results and strengthen confidence in AI. In fact, the first algorithms have already been tested and evaluated in an industrial environment. Now, the scalability of the procedures needs to be addressed. Next year, an important milestone will follow: then the research team would like to present the first prototype for the data-driven creation of project plans.
The HPI research project is extremely application-oriented – after all, it aims to alleviate one of the most significant pain points in the IT industry, one which consumes enormous resources. According to Prof. Jürgen Döllner "In the IT field, we apply 'engineering-like' rationality mostly to our code, but surprisingly rarely to our own working methods and production processes. That's why I'm delighted that Daniel's project works so closely with the mission of Digital Engineering, which is ultimately largely the initiative of our colleague and benefactor Prof. Hasso Plattner. With the term Digital Engineering we express our ambition to let practical software production mature in every respect into a real engineering science, i.e. one that reflects its own approach," says Döllner. The research group and HPI will share their results with the scientific community and look forward to discussion, expert input, and cooperation requests.
The Seerene Digital Engineering Platform provides IT decision-makers with the information needed to actively drive their organization towards software excellence. The analytics technology extracts and fuses the data traces left by all the tools in your software development infrastructure and derives insights of high strategic and operational relevance for both managers as well as development experts.
Authors: Oliver Viel, Dr. Uta Morgenstern
Translated by Brandon Lewis