CONFERENCE PROGRAM OF 2021

Please note:
On this site, there is only displayed the English speaking sessions of the OOP 2021 Digital. You can find all conference sessions, including the German speaking ones, here.

Theme: Artificial Intelligence

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  • Tuesday
    09.02.
  • Wednesday
    10.02.
  • Thursday
    11.02.
, (Tuesday, 09.February 2021)
14:00 - 14:45
Di 8.2
Back to the Data - Now That We (Machine) Learned From Test Results, What Else Did We Gain?
Back to the Data - Now That We (Machine) Learned From Test Results, What Else Did We Gain?

80% of machine learning is said to be data wrangling. Is all this wasted effort? Hardly - often the journey really is its own reward.

In this talk, we'll briefly describe a machine learning project that predicts the outcome of test cases in a large-scale software development cycle. We'll then show what we gained from collecting the necessary data and how these insights can have lasting impact on the day-to-day work of developers, testers and architects. This includes a quick answer to the well-known question: Whose defect is it anyway?

Target Audience
: Developers, Testers, Architects
Prerequisites: Basic knowledge of software development and testing and an interest in data analytics
Level: Basic

Extended Abstract
:
Data science folk wisdom holds that at least 80% of machine learning consists of data wrangling, i.e. finding, integrating, annotating, and cleaning the necessary data.

While sometimes viewed as less "glorious" than the deployment of powerful models, this journey has its own rewards as well.

Benefits may sometimes be somewhat expectable, if still non-trivial, like data cleaning potentially exposing errors in underlying data bases.

In other cases, though, there may be some low-hanging fruit a casual glance might miss even though they are indeed rewarding.

Data integration often reveals opportunities for statistical analyses that are relatively simple, but may still have high impact.

In this talk, we'll start at the destination: the result of a machine learning project where we successfully predicted test results from code changes.

A necessary task was the integration of several data sources from the full software development cycle - from code to testing to release in a large industry project with more than 500 developers.
Naturally, this required all typical steps in the data science cycle: building up domain knowledge and problem understanding, both semantic and technical data integration, data base architecture and administration, machine learning feature design, model training and evaluation, and communicating results to stakeholders.

These steps yielded several benefits, on which we will focus in our talk.

Among others these include data quality insights (e.g. about actual "back to the future" timestamps), and new analyses which were made possible by a unified view of the data (e.g. survival analysis of defects).

Last but not least, we demonstrate a simple answer to a well-known question, especially in larger software development contexts: Whose defect is it anyway - how can we avoid assigning defects to teams that have nothing to do with them?

Thanks to the integrated information sources from systems concerned with version control, test result logging, and defect management, we are able to support the claims made in this talk with statistics taken from 6 years of real-world data.

Gregor Endler holds a doctor's degree in Computer Science for his thesis on completeness estimation of timestamped data. His work at codemanufaktur GmbH deals with Machine Learning and Data Analysis.
Marco Achtziger is Test Architect working for Siemens Healthcare GmbH in Forchheim. He has several qualifications from iSTQB and iSQI and is a certified Software Architect by Siemens AG.
Gregor Endler, Marco Achtziger
Gregor Endler, Marco Achtziger
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, (Wednesday, 10.February 2021)
09:00 - 10:45
Mi 5.1
Software 2.0 - Building Production-Grade AI Enabled Products
Software 2.0 - Building Production-Grade AI Enabled Products

AI is maybe the most powerful tool our generation has available. Andrew NG called it "the new electricity". But what does it take to build AI enabled products? What are the key elements to achieve production grade AI? How does it impact your development process? How can quality be achieved? These are the questions this talk tries to answer. You will get an idea why the industry is talking about nothing less than a paradigm shift when it comes to developing AI based products.

Target Audience: Everyone interested in the shift from classical software engineering to data driven AI applications
Prerequisites: Interested in AI, how it works and its impact on engineering departments
Level: Advanced

Extended Abstract:
AI is maybe the most powerful tool our generation has available. Andrew NG called it "the new electricity". Most likely you used an AI based product within the last 3 hours, maybe without even noticing it. But what does it take to build AI enabled products? What are the key elements to achieve production grade AI? How does it impact your development process? How can quality be achieved? These are the questions this talk tries to answer. In addition we will look into the different stages of AI development and the tools which can help to make this process more efficient. You will get an idea why the industry is talking about nothing less than a paradigm shift when it comes to developing AI based products.

Daniel Rödler is a Product Manager at understand.ai with the mission to automate annotations for autonomous vehicles and responsible for the overall product strategy. Before joining understand.ai Daniel worked for LogMeIn, a company focusing on online collaboration. There he was responsible for a part of GoToMeeting, LogMeIns biggest product with more than 2 Million users per month including an AI based voice identification mechanism to achieve much more useful meeting transcripts.
DevOps: State of the Union
DevOps: State of the Union

Whether evolution or revolution, or yet old wine in new skins, for more than 10 years, DevOps is changing how we develop and deliver software. This session looks back on the roots of DevOps, its movement until today, and current as well as possible future directions. This interactive session aims to offer a set of fruitful starting points for reflection and discussions.

Target Audience: Anyone interested in developing and delivering software
Prerequisites: Knowledge in DevOps and agile software development
Level: Advanced

Michael Hüttermann is a freelancing DevOps consultant. Besides that, he is a researcher studying DevOps. 
Daniel Rödler
Michael Hüttermann
Daniel Rödler

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Michael Hüttermann
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14:30 - 15:30
Mi 8.3
Validation of Autonomous Systems
Validation of Autonomous Systems

Autonomous and automated systems are increasingly being used in IT such as finance, but also transport, medical surgery and industry automation. Yet, the distrust in their reliability is growing. This presentation introduces the validation of autonomous systems. We evaluate in practical situations such as automatic driving and autonomous robots different validation methods. The conclusion: Classic methods are relevant for coverage in defined situations but must be supplemented with cognitive test methods and scenario-based testing.

Target Audience: Testers, Quality assurance, Architects, Requirements Engineers, Product Owners, Software Engineers
Prerequisites: Testing basic know-how
Level: Advanced

Extended Abstract:
Autonomous and automated systems are increasingly being used in IT such as finance, but also transport, medical surgery and industry automation. Yet, the distrust in their reliability is growing. There are many open questions about the validation of autonomous systems: How to define reliability? How to trace back decision making and judge afterwards about it? How to supervise? Or, how to define liability in the event of failure? The underlying algorithms are difficult to understand and thus intransparent. Traditional validations are complex, expensive and therefore expensive. In addition, no repeatable effective coverage for regression strategies for upgrades and updates is available, thus limiting OTA and continuous deployment.

With artificial intelligence and machine learning, we need to satisfy algorithmic transparency. For instance, what are the rule in an obviously not anymore algorithmically tangible neural network to determine who gets a credit or how an autonomous vehicle might react with several hazards at the same time? Classic traceability and regression testing will certainly not work. Rather, future verification and validation methods and tools will include more intelligence based on big data exploits, business intelligence, and their own learning, to learn and improve about software quality in a dynamic way.

Verification and validation depend on many factors. Every organization implements its own methodology and development environment, based on a combination of several of the tools presented in this presentation. It is however relevant to not only deploy tools, but also build the necessary verification and validation competences. Too often we see solid tool chains, but no tangible test strategy. To mitigate these pure human risks, software must increasingly be capable to automatically detect its own defects and failure points.

Various new intelligent validation methods and tools are evolving which can assist in a smart validation of autonomous systems.

This presentation introduces the validation of autonomous systems. We evaluate in practical situations such as automatic driving and autonomous robots different validation methods. The conclusion: Classic methods are relevant for coverage in defined situations but must be supplemented with cognitive test methods and scenario-based testing.

Christof Ebert is managing director at Vector Consulting Services. He supports clients around the world in product development. Before he had been working for twelve years in global senior management positions. A trusted advisor for companies around the world and a member of several of industry boards, he is a professor at the University of Stuttgart and at Sorbonne in Paris. He authored several books including the most recent “Global Software and IT” published by Wiley and "Requirements Engineering" published by dPunkt and in China by Motor Press. Since many years he is serving on the editorial Board of the prestigious "IEEE Software" journal.
Michael Weyrich is the director of the University of Stuttgart’s Institute for Automation and Software Systems. Before he spent many years at Daimler and Siemens where he had senior management positions in engineering. Ever since he engages in technology transfer and is heading numerous industry projects on automation and validation. He authored several books including the leading reference book on "Industrial Automation" published by Springer. Since many years he serves on VDI in various leadership positions. He is leading the VDI/VDE committee on testing of connected systems and industry 4.0.
University of Stuttgart - Institute of Industrial Automation and Software Engineering
Academic staff

Christof Ebert, Michael Weyrich, Benjamin Lindemann
Christof Ebert, Michael Weyrich, Benjamin Lindemann
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, (Thursday, 11.February 2021)
09:00 - 10:30
Do 5.1
Software Architecture for AI-intensive Systems
Software Architecture for AI-intensive Systems

The problem at hand is partly the application of software engineering best practices to AI, but more so the evolution of software engineering to attend to software-intensive systems that contain AI components. In this lecture, I'll examine both dimensions: emerging AI architectures, neuro-symbolic systems, designing/testing/deploying/refactoring/maintaining systems with AI components; the future of software engineering.

Target Audience:
Software engineers
Prerequisites: Curiosity and a desire to think different
Level: Advanced

Grady Booch is Chief Scientist for Software Engineering at IBM Research where he leads IBM’s research and development for embodied cognition. Having originated the term and the practice of object-oriented design, he is best known for his work in advancing the fields of software engineering and software architecture. A co-author of the Unified Modeling Language (UML), a founding member of the Agile Alliance, and a founding member of the Hillside Group, Grady has published six books and several hundred technical articles, including an ongoing column for IEEE Software. Grady was also a trustee for the Computer History Museum. He is an IBM Fellow, an ACM and IEEE Fellow, has been awarded the Lovelace Medal and has given the Turing Lecture for the BCS, and was recently named an IEEE Computer Pioneer. He is currently developing a major trans-media documentary for public broadcast on the intersection of computing and the human experience.
Grady Booch
Grady Booch
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