Open source and AI keynote

Jun 17, 2021Download as PPTX, PDF1 like105 views

The document discusses open source software and IBM Z. It notes that the latest innovations in areas like augmented reality, virtual reality, and artificial intelligence are all based on open source technologies. It highlights some key open source statistics like over 210 million repositories and 500,000 projects. The rest of the document focuses on encouraging open source contribution and consumption, providing examples of open source projects relevant to IBM Z, and use cases that apply machine learning to real world problems using open source tools and libraries.

Open Source Software and IBM Z
Javier Perez | Open Source Leader | IBM Z
@jperezp_bos
javier.perez@ibm.com
javierperez.mozello.com
www.linkedin.com/in/javierperez

Innovation & Open Source
Latest Innovations are all Open
Augmented Reality, Virtual Reality,
Autonomous cars, AI, ML, DL,
Blockchain, Virtual Assistance,…
All Programming Languages and
Frameworks are Open Source

210M+
Repositories
500K+
Open-Source Projects
Open Source Projects
56M+
Developers

200+
Projects
450+
Projects
420+
Projects
Open Source Foundations

1.5M +
869 packages per day
376K +
142 packages per day
297K +
101 packages per day
286K +
207 packages per day
241K +
126 packages per day
164K +
15 packages per day
Millions of Open-Source Libraries
Source: Feb. 1, 2021 www.modulecounts.com

Survey: 3,400 Developer
Consider open source software
better than proprietary software
Developer Managers say
open source knowledge is a
Factor for Hiring Decisions
Agree that contributions impress
potential employers
94 %
65 %
87 %
Photo by Michèle Eckert on Unsplash
Source: 2021 O'Reilly Media survey, commissioned by IBM

Downstream & Upstream
Consume:
• Reuse open source software
• Build on top of open source software
• Monetize your software
Contribute:
• Help the project, be a “good neighbor”
• Deliver innovation back to the project
• Advocate for quality and security
• Meaningful contributions foster community
Free photo by Chris Urbanowicz

Take Action
As a Developer create or review:
• Open source license
• README
• Contribution guidelines & code of
conduct
Contributions:
• Enhancements
• Innovation
• Bug fixes
• Vulnerability fixes
• Port to new platform

Contribute not just Code
• Test
• Write documentation
• Translate
• Bug triaging
• Suggest a feature
• Graphic design
• Write a blog
• Give a talk

Open Source Software Ecosystem for IBM Z
Linux Distributions &
Virtualization
Networking & Monitoring
Cloud & Container Services Languages & Runtimes DevOps/Automation
Linux Community Versions
Big Data, Observability, Analytics
Databases & Storage
Middleware & others
ClefOS
www.ibm.com/community/z/open-source-software/
hub.docker.com Memcached
More:

• Python
• Cassandra
• Kafka
• Spark
• Apache Airflow
• PostgreSQL
• Hadoop
• Storm
• Flume
• Kinesis
Financial Industry ML OSS
• TensorFlow
• SPARQL
• Hive
• MongoDB
• Oozie
• Apache Beam
• PySpark
• PyTorch
• R
• Kotlin

Fan Open Debate Questions
• Searches more than 40 million pieces of
unstructured data such as sports news and
analysis articles. Pros and cons for online
debates
Match Insights Factsheets
• Generate factsheets before matches by querying
14 million articles from more than 100K sites
ML Audience noise in broadcast
• Analysis of previous years crowd reactions to
determine moments of excitement for highlights
ML Use Case: US Open 2020

ML helps fans make more informed fantasy
lineup and trade decisions by giving them new
insights (over 25 billions insights)
• Read and comprehend hundreds of millions of
articles (228 million in 2019)
• Apply NLP to determine boom/bust and
scoring spreads
• Information for fair market valuation and cost
for player trades
ESPN Fantasy Football

A fully-autonomous, AI powered marine research
vessel to travel from Plymouth UK to Plymouth, MA
• Onboard cameras, radar, sensors, GPS,
geospatial information
• 2 years of ML trained models with millions of
nautical images
• Operation Decision Management to identify
“unsafe” situations like weather and computer
vision to avoid hazards such as land, debris
and other ships
Mayflower Autonomous Ship
mas400.com/technology

● Is all about Open Source
● AI/ML/DL is based on Open Source
● Consume & Contribute to Open Source
● Build something great!
Photo by Prateek Katyal from Pexels
Key Takeaways

Thank you!
Javier Perez | Open Source Leader | IBM Z
@jperezp_bos
javier.perez@ibm.com
javierperez.mozello.com
www.linkedin.com/in/javierperez

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Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora

Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.

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