The adoption of static analysis of C++ and Java requires that the findings and errors can be prioritised in an efficient way. Our work shows that Machine learning (ML) can support this presentation of static analysis results to end-users. The ML engine learns from the codebase itself, and also observes the violations that the user fixes and which he ignores. The ML uses this to suggest the next best violations to fix, relying on probability of violations to be harmful or most likely to be a noise.

Target Audience: Developer Managers, R&D Managers, Software Architects, Software Engineers
Prerequisites: English, Software development, Coding experience, C++, Java, C#.
Level: Expert

Extended Abstract
Static code analysis is often understood as a mandatory part for checking the source code compliance to government and industry regulations, company-wide guidelines and practices. It can play, however, a more fundamental role in estimating the quality of the code in general, understanding the amount of technical debt, creating the strategy to reduce the amount of technical debt, as well as a helper in making decisions on how to speed up the development by creating a more maintainable, understandable, sociable codebase.

However, by its nature, static code analysis is bound to produce a large amount of noise and false alerts that can distract the team from the actual bugs in the code and prevent them from working thoroughly with the findings. One of the reasons for that is the level of soundness of the static analysis tool. If we want to be sure that the analysis is bound to find all errors in the code, the static analysis tool has to report all possible candidates. The more sound the tool is configured to be, the larger the number of the possible errors is reported, which increases the number of false positives as well.

To improve the user experience of working with static analysis technology, we have developed a machine learning (ML) based approach to presenting the results of the static analysis to users. The ML engine can learn from the code base itself, from a user's preferences, as well as from the interaction within the team. At the code level, our engine learns from the syntactical and semantical structure of the analyzed code to understand which violations are more likely to cause more harm, which violations are more likely to be noise, what underlying problems can be fixed to drastically reduce the number of reported violations. At the user level, the ML engine observes which violations the user fixes and which violations the user ignores. Based on these observations, the ML engine builds a model and uses it to suggest the next best violations to fix.

Maintaining a database containing millions of products can be very challenging, especially when the information you require of these products is subject to changes over time.

We show how we used state of the art Deep Learning methods (such as Transformers, BERT) in connection with smart text matching in order to extract relevant information from free-form text.

We also explain how we leveraged the existing database to create an automatically labelled training dataset.

Our model enables us to continuously update idealos database automatically.

Target Audience: Decision Makers, Technical Project Leaders, Developers
Prerequisites: Basic knowledge of machine learning methodology
Level: Advanced

Extended Abstract

To maintain idealos product base, product information in the form of values of predefined product attributes needs to be extracted from free-form text product descriptions.

Before the use of a Machine Learning based solution, this process required a lot of manual work to define rules to extract this information. There is also a high effort connected to keeping these rules consistent across the whole database and different types of products, especially since the source of this information (the product descriptions) as well as the required information (the product properties) are subject to changes over time.

In this talk we present a machine learning solution, based on fine tuned state-of-the-art models such as BERT, which is able to extract product information automatically from product descriptions with production-ready performance.

Our solution contains two different models, each following one of the well-known problem settings in Natural Language Processing (NLP): Semantic Segmentation of text (also known as Token Classification) and Question Answering. We will present both models in detail, as well as discussing their advantages and disadvantages for solving the task at hand and how we measured its performance (metrics).

We will also emphasize the importance of identifying aspects of your data that ensure that the developed model can actually fulfill your business needs before curating your dataset.

This highlights another benefit of implementing a Machine Learning model for a huge database: You will get sanity checks of your existing data “for free”, as consistent data is a prerequisite for a successful Machine Learning project.

One problem that is very common in large organisations is that there is often no or only very little training data in the form of labels for specific text sections available. We show how we mitigated this problem by leveraging the existing database to generate a large artificial training dataset. This allowed us to only use a few thousand manually labelled examples for training and testing to reach sufficient performance.

Der Umfang der MLOps-Prozesse und -Technologien hängt von dem AI "Maturity Level" der jeweiligen Organisation ab. Das Umsetzen von kanonischem MLOps kann zu einer unnötigen Komplexität in der Architektur und Organization führen. Das “AI Readiness”-Framework definiert den Reifegrad einer Organisation bei der Nutzung von ML/AI und ordnet es in eine der drei Stufen ein: Tactical, Strategic und Transformational. In dem Vortrag gehe ich auf die drei “AI-Readiness”-Stufen ein und zeige, wie man eine MLOps Roadmap ableitet.

Zielpublikum: Architekt:innen, Entwickler:innen, Projektleiter:innen, Machine Learning-Enthusiasten, Data Scientists
Voraussetzungen: Allgemeines Verständnis über Machine Learning
Schwierigkeitsgrad: Fortgeschritten

Extended Abstract

Der Umfang der MLOps-Prozesse und -Technologien hängt von dem AI "Maturity Level" der jeweiligen Organisation ab.

Das “AI Readiness”-Framework definiert den Reifegrad einer Organisation bei der Nutzung von ML/AI und ordnet es in eine der drei Stufen ein:

Tactical, Strategic und Transformational.

Es ist essenziell zu verstehen, dass die Verwendung von Machine Learning Tools und Techniken in diesen Phasen in einem Reifeprozess verläuft. Von dem manuellen und dem Ad-hoc-Deployment, über die Implementierung von ML Workflows/Pipelines, bis hin zu Continuous Training und dem automatisierten Deployment von ML-Modellen.

Im Vortrag gehe ich auf diese drei “AI-Readiness”-Stufen ein und zeige, wie man die nächsten Schritte (MLOps Roadmap) ableitet

Machine Learning wird häufig immer noch als eine mathematische Herausforderung angesehen und an den Universitäten meist ebenso gelehrt. Es regt sich aber auch im akademischen Umfeld Widerstand und selbst Stars wie Andrew Ng bieten neuerdings Kurse mit Inhalten an, die uns Softwareentwickeln eher vertraut sind.
In diesem Talk werde ich beleuchten, wie viel man wirklich wissen muss, um als Softwareentwickler Machine Learning sinnvoll einzusetzen.

Zielpublikum: Jede Rolle im Bereich Softwareentwicklung
Voraussetzungen: Verständnis von Softwareentwicklung, kein Verständnis von ML notwendig
Schwierigkeitsgrad: Anfänger

Extended Abstract
Machine Learning Verfahren im allgemeinen und Neuronale Netzwerke im speziellen erfordern für ihr tiefes Verständnis Kenntnisse im Bereich der Linearen Algebra, der Analysis und der Statistik.
Aber: Als Softwareentwickler implementiert man Neuronale Netzwerke typischerweise nicht auf der grünen Wiese und kommt auch nicht in die Verlegenheit, komplexe Netzwerke selbst aufzubauen. Viel häufiger kommen Frameworks wie TensorFlow und sogar komplett aufgebaute und zum Teil schon trainierte Neuronale Netzwerke zum Einsatz.
Das verschiebt die Problematik des Machine Learnings in die Richtung des Engineerings:

• Wie baut man eine Trainings-Pipeline auf?
• Wie bekommt man das trainierte Modell in Produktion und sorgt dann dafür, dass das Modell weiterhin aktuell bleibt?

Agile is becoming a standard delivery method adopted by organizations across the globe, according to VersionOne’s 11th Annual State of Agile Report. While 94 percent of survey respondents said their organizations practiced Agile, 80 percent said their organization was at or below a “still maturing” level. There are multiple reasons on why the Agile maturity of the teams are low, but the key one is teams look at Agile as a process change rather than a cultural change.
At Accenture we have been delivering solutions using Agile practices and principles and based on our experience we do face the challenges within the Agile teams such as the whole team’s limited experience with Agile, slowing down of work due to limited access to Product Owner, incomplete\less detailed user stories leading to high onshore dependency and struggling to keep momentum with continuous churn of Agile events through active participation and to maintain quality of artefacts (backlog, burndown, impediment list, retrospective action log etc.). These challenges manifold when delivery teams are practicing distributed Agile at scale. Apart from these challenges, at Accenture we are focused on how to make our teams more productive and ease the ways of working for the Agile teams.
In this presentation, we share our experiences in leveraging AI for Agile and the team feedback where these solutions were deployed. Did the AI in Agile help to address or mitigate the challenges it was introduced to address?