The document discusses automatic test generation for space applications. It describes a master's thesis that aims to automatically generate tests for an operational simulator using model-based and constraint-based testing. The thesis will extract UML diagrams and invariants from existing code and relate them to requirements. Tools like Korat and Pex were studied, with Korat able to generate complex data structures that satisfy invariants, while Pex can achieve high code coverage but does not maintain data structure invariants. The work will combine static analysis and data structure generation to automatically test the operational simulator.
Eclipse science group presentation given at Eclipse Converge and Devoxx 2017 in California. These slides give an overview of projects in the Eclipse Science working group in 2017.
Log Analytics in Datacenter with Apache Spark and Machine LearningPiotr Tylenda
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Mining Frequent Closed Graphs on Evolving Data StreamsAlbert Bifet
Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this talk we present a framework for studying graph pattern mining on time-varying streams and large datasets.
Eclipse science group presentation given at Eclipse Converge and Devoxx 2017 in California. These slides give an overview of projects in the Eclipse Science working group in 2017.
Log Analytics in Datacenter with Apache Spark and Machine LearningPiotr Tylenda
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Mining Frequent Closed Graphs on Evolving Data StreamsAlbert Bifet
Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this talk we present a framework for studying graph pattern mining on time-varying streams and large datasets.
ScalaDays 2013 Keynote Speech by Martin OderskyTypesafe
Scala gives you awesome expressive power, but how to make best use of it? In my talk I will discuss the question what makes good Scala style. We will start with syntax and continue with how to name things, how to mix objects and functions, where (and where not) to use mutable state, and when to use which design pattern. As most questions of style, the discussion will be quite subjective, and some of it might be controversial. I am looking forward to discuss these topics with the conference attendees.
Temporal logic and functional reactive programmingSergei Winitzki
In my day job, most bugs come from imperatively implemented reactive programs. Temporal Logic and FRP are declarative approaches that promise to solve my problems. I will briey review the motivations behind
and the connections between temporal logic and FRP. I propose a rather "pedestrian" approach to propositional linear-time temporal logic (LTL), showing how to perform calculations in LTL and how to synthesize programs from LTL formulas. I intend to explain why LTL largely failed to
solve the synthesis problem, and how FRP tries to cope.
FRP can be formulated as a -calculus with types given by the propositional intuitionistic LTL. I will discuss the limitations of this approach, and outline the features of FRP that are required by typical application programming scenarios. My talk will be largely self-contained and should be understandable to anyone familiar with Curry-Howard and functional programming.
EVERYTHING ABOUT STATIC CODE ANALYSIS FOR A JAVA PROGRAMMERAndrey Karpov
Theory
Code quality (bugs, vulnerabilities)
Methodologies of code protection against defects
Code Review
Static analysis and everything related to it
Tools
Existing tools of static analysis
SonarQube
PVS-Studio for Java what is it?
Several detected examples of code with defects
More about static analysis
Conclusions
Polyglot persistence for Java developers - moving out of the relational comfo...Chris Richardson
Relational databases have long been considered the one true way to persist enterprise data. But today, NoSQL databases are emerging as a viable alternative for many applications. They can simplify the persistence of complex data models and offer significantly better scalability, and performance. But using NoSQL databases is very different than the ACID/SQL/JDBC/JPA world that we have become accustomed to. They have different and unfamiliar APIs and a very different and usually limited transaction model. In this presentation, we describe some popular NoSQL databases – Redis, MongoDB, and Cassandra. You will learn about each database’s data model and Java API. We describe the benefits and drawbacks with using NoSQL databases. Finally, you will learn how the Spring Data project simplifies the development of Java applications that use NoSQL databases.
Sets, maps and hash tables (Java Collections)Fulvio Corno
Sets, maps and hash tables in the Java Collections framework
Teaching material for the course of "Tecniche di Programmazione" at Politecnico di Torino in year 2012/2013. More information: http://bit.ly/tecn-progr
Relaxation methods for the matrix exponential on large networksDavid Gleich
My talk from the Stanford ICME seminar series on doing network analysis and link prediction using the a fast algorithm for the matrix exponential on graph problems.
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O.ai's Distributed Deep Learning by Arno Candel 04/03/14Sri Ambati
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
http://docs.0xdata.com/datascience/deeplearning.html
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Log Analytics in Datacenter with Apache Spark and Machine LearningAgnieszka Potulska
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Anti-differentiating Approximation Algorithms: PageRank and MinCutDavid Gleich
We study how Google's PageRank method relates to mincut and a particular type of electrical flow in a network. We also explain the details of how the "push method" for computing PageRank helps to accelerate it. This has implications for semi-supervised learning and machine learning, as well as social network analysis.
San Francisco Hadoop User Group Meetup Deep LearningSri Ambati
Hadoop User Group, San Francisco, Dec 10 2014.
Video: http://new.livestream.com/accounts/10932136/events/3649553 (starting at 48 minutes)
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice for highest predictive performance in traditional business analytics. This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of business-critical problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization, parameter tuning and a fully-featured R interface. World record performance on the classic MNIST dataset, best-in-class accuracy for a high-dimensional eBay text classification problem and other relevant datasets showcase the power of this game-changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
Bio:
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named 2014 Big Data All-Star by Fortune Magazine.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
ScalaDays 2013 Keynote Speech by Martin OderskyTypesafe
Scala gives you awesome expressive power, but how to make best use of it? In my talk I will discuss the question what makes good Scala style. We will start with syntax and continue with how to name things, how to mix objects and functions, where (and where not) to use mutable state, and when to use which design pattern. As most questions of style, the discussion will be quite subjective, and some of it might be controversial. I am looking forward to discuss these topics with the conference attendees.
Temporal logic and functional reactive programmingSergei Winitzki
In my day job, most bugs come from imperatively implemented reactive programs. Temporal Logic and FRP are declarative approaches that promise to solve my problems. I will briey review the motivations behind
and the connections between temporal logic and FRP. I propose a rather "pedestrian" approach to propositional linear-time temporal logic (LTL), showing how to perform calculations in LTL and how to synthesize programs from LTL formulas. I intend to explain why LTL largely failed to
solve the synthesis problem, and how FRP tries to cope.
FRP can be formulated as a -calculus with types given by the propositional intuitionistic LTL. I will discuss the limitations of this approach, and outline the features of FRP that are required by typical application programming scenarios. My talk will be largely self-contained and should be understandable to anyone familiar with Curry-Howard and functional programming.
EVERYTHING ABOUT STATIC CODE ANALYSIS FOR A JAVA PROGRAMMERAndrey Karpov
Theory
Code quality (bugs, vulnerabilities)
Methodologies of code protection against defects
Code Review
Static analysis and everything related to it
Tools
Existing tools of static analysis
SonarQube
PVS-Studio for Java what is it?
Several detected examples of code with defects
More about static analysis
Conclusions
Polyglot persistence for Java developers - moving out of the relational comfo...Chris Richardson
Relational databases have long been considered the one true way to persist enterprise data. But today, NoSQL databases are emerging as a viable alternative for many applications. They can simplify the persistence of complex data models and offer significantly better scalability, and performance. But using NoSQL databases is very different than the ACID/SQL/JDBC/JPA world that we have become accustomed to. They have different and unfamiliar APIs and a very different and usually limited transaction model. In this presentation, we describe some popular NoSQL databases – Redis, MongoDB, and Cassandra. You will learn about each database’s data model and Java API. We describe the benefits and drawbacks with using NoSQL databases. Finally, you will learn how the Spring Data project simplifies the development of Java applications that use NoSQL databases.
Sets, maps and hash tables (Java Collections)Fulvio Corno
Sets, maps and hash tables in the Java Collections framework
Teaching material for the course of "Tecniche di Programmazione" at Politecnico di Torino in year 2012/2013. More information: http://bit.ly/tecn-progr
Relaxation methods for the matrix exponential on large networksDavid Gleich
My talk from the Stanford ICME seminar series on doing network analysis and link prediction using the a fast algorithm for the matrix exponential on graph problems.
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O.ai's Distributed Deep Learning by Arno Candel 04/03/14Sri Ambati
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
http://docs.0xdata.com/datascience/deeplearning.html
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Log Analytics in Datacenter with Apache Spark and Machine LearningAgnieszka Potulska
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Anti-differentiating Approximation Algorithms: PageRank and MinCutDavid Gleich
We study how Google's PageRank method relates to mincut and a particular type of electrical flow in a network. We also explain the details of how the "push method" for computing PageRank helps to accelerate it. This has implications for semi-supervised learning and machine learning, as well as social network analysis.
San Francisco Hadoop User Group Meetup Deep LearningSri Ambati
Hadoop User Group, San Francisco, Dec 10 2014.
Video: http://new.livestream.com/accounts/10932136/events/3649553 (starting at 48 minutes)
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice for highest predictive performance in traditional business analytics. This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of business-critical problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization, parameter tuning and a fully-featured R interface. World record performance on the classic MNIST dataset, best-in-class accuracy for a high-dimensional eBay text classification problem and other relevant datasets showcase the power of this game-changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
Bio:
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes. He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named 2014 Big Data All-Star by Fortune Magazine.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O Distributed Deep Learning by Arno Candel 071614Sri Ambati
Deep Learning R Vignette Documentation: https://github.com/0xdata/h2o/tree/master/docs/deeplearning/
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice in traditional business analytics.
This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of enterprise-scale problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization and optimization for class imbalance. World record performance on the classic MNIST dataset, best-in-class accuracy for eBay text classification and others showcase the power of this game changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
About the Speaker: Arno Candel
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes.
He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
a paper review. This presentation introduces Abductive Commonsense Reasoning which is the published paper in ICLR 2020. In this paper, the authors use commonsense to generate plausible hypotheses. They generate new data set 'ART' and propose new models for 'aNLI', 'aNLG' using BERT, and GPT.
Presented online for C++ on Sea (2020-07-17)
Video at https://www.youtube.com/watch?v=Bai1DTcCHVE
Lambdas. All the cool kid languages have them. But does lambda mean what C++ and other languages, from Java to Python, mean by lambda? Where did lambdas come from? What were they originally for? What is their relationship to data abstraction?
In this session we will into the history, the syntax, the uses and abuses of lambdas and the way in which lambda constructs in C++ and other languages do (or do not) match the original construct introduced in lambda calculus.
A package system for maintaining large model distributions in vle softwareDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
A preliminary study of diversity in ELM ensembles (HAIS 2018)Carlos Perales
Presentation in the International Conference on Hybrid Artificial Intelligent Systems (HAIS) 2018 of a preliminary study of diversity in ensembles, applied to Extreme Learning Machine (ELM)
Functional Programming You Already KnowKevlin Henney
Presented at NDC 2013 in Oslo (13th June 2013)
Video available on Vimeo: https://vimeo.com/68327245
From JVM to .NET languages, from minor coding idioms to system-level architectures, functional programming is enjoying a long overdue surge in interest. Functional programming is certainly not a new idea and, although not apparently as mainstream as object-oriented and procedural programming, many of its concepts are also more familiar than many programmers believe.
This talk examines functional and declarative programming styles from the point of view of coding patterns, little languages and programming techniques already familiar to many programmers.
Similar to Automatic Test Generation for Space (20)
The European Space Agency (ESA) uses an engine to perform tests in the Ground Segment infrastructure, specially the Operational Simulator. This engine uses many different tools to ensure the development of regression testing infrastructure and these tests perform black-box testing to the C++ simulator implementation. VST (VisionSpace Technologies) is one of the companies that provides these services to ESA and they need a tool to infer automatically tests from the existing C++ code, instead of writing manually scripts to perform tests. With this motivation in mind, this paper explores automatic testing approaches and tools in order to propose a system that satisfies VST needs.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Automatic Test Generation for Space
1. Automatic Test Generation for Space
Ulisses Costa Daniela da Cruz Pedro Rangel Henriques
SLATE’12 - Symposium on Languages, Applications and Technologies
June 21, 2012
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
2. Context
Problem
VST (Visionspace Technologies) provides services related to testing
for ESA and wants to automate the test generation for the
Operational Simulator platform.
This presentation appears in the context of a master’s thesis that
aims at:
Generate automatically tests for the Operational Simulator
Generate unit tests for the Operational Simulator language –
C++
Parametrize the size of the generated data structures and be
able to configure other attributes
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
3. Motivation
Extract UML and OCL from the existing code
Extract tests from the existing code
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
4. OCL Inference
OCL is a language used to describe logic properties about UML
models, typically in the form of invariants.
The first step is to extract interesting invariants from the code and
match them with requirements.
Generate UML diagrams from the existing code (easy)
Infer code invariants (hard)
Relate the discovered invariants with the UML diagrams
Relate the discovered diagrams with requirements
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
5. White vs. Black Box Testing
Types of tests regarding the knowledge about the code
White Box , there is knowledge about the code, and this
knowledge is used to perform the test generation.
Black Box , there is only knowledge about the requirements and
about how each component should behave.
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
6. Approaches 1/2
Specification-based Generation Testing , aka Model Based Testing
consists in testing a program based on the program
specification or on the program model. Test cases
can be generated from the specification, without
consider the code.
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
7. Approaches 2/2
Constraint-based Generation Testing , can be used to select test
cases that meet some variable restrictions. When
combined with symbolic execution, gathers
restrictions along the different paths in the CFG. It is
possible to solve these restrictions and generate test
cases.
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
8. Current state
By now, we studied different approaches and tools, the more
important to our goal are:
Korat, is a mature framework to automatically construct
complex structures for JAVA
Pex is a White-box testing framework from Microsoft tool
that tries to give total code coverage
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
9. Studied Tools - Pex
1 public class Program {
2 public static int BSearch ( int x , int n ) {
3 return BinarySearch (x , 0 , n ) ;
4 }
5 static int BinarySearch ( int x , int lo , int hi ) {
6 while ( lo < hi ) {
7 int mid = ( lo + hi ) /2;
8 Debug . Assert ( mid >= lo && mid < hi ) ;
9 if ( x < mid ) { hi = mid ; } else { lo = mid +1; }
10 }
11 return lo ;
12 }
13 }
Result x n result Output/Exception
0 0 0
0 1 1
0 3 1
1073741888 1719676992 TraceAssertionException
1 6 2
50 96 51
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
10. Studied Tools - Korat
1 public class LinkedList T {
2 public static class LinkedListElement T {
3 public T Data ;
4 public LinkedListElement T Prev ;
5 public LinkedListElement T Next ;
6 }
7 private LinkedListElement T Head ;
8 private LinkedListElement T Tail ;
9 private int size ;
10 }
LinkedList class invariants (circular doubly linked list):
∀ l : l ∈ LinkedList : Head(l) ≡ null ∨ Tail(l) ≡ null ⇔ size(l) ≡ 0 (1)
∀ l : l ∈ LinkedList : Tail(l).Next ≡ null (2)
∀ l : l ∈ LinkedList : Head(l).Prev ≡ null (3)
∀ l : l ∈ LinkedList : size(l) ≡ 1 ⇔ Head(l) ≡ Tail(l) (4)
∀ l : l ∈ LinkedList : ∀ e1 , e2 : {e1 , e2 } ⊆ l : ∃ e : e ∈ l : e1 .Next ≡ e ∧ e2 .Prev ≡ e
⊆ ∈ (5)
∀ l : l ∈ LinkedList : ∀ e1 , e2 : {e1 , e2 } ⊆ l : e1 ≡ e2 ⇒ i(e1 ) ≡ i(e2 )
⊆ (6)
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
11. Studied Tools - Pex - LinkedList
(a) LinkedList instance (b) LinkedList instance
generated by Pex to test generated by Pex to test
the method Remove the method Find
Figure: Examples of instances generated by Pex to the LinkedList class.
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
12. Studied Tools - Korat - LinkedList
(a) LinkedList (b) LinkedList
instance with 2 instance with 5
elements elements
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
13. Studied Tools - Summary
Summary
Pex uses static analysis and is very efficient in discovering all the
possible execution paths in C# methods. Pex can also be used to
generate classes testcases, but the generated instances does not
keep the invariants of data structures.
On the other hand, Korat is the ideal tool to generate data
structures that meet the invariants.
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
14. Conclusion and Future work
Pex has proved to be a powerful tool regarding full coverage.
Korat is a very useful tool to generate complex data
structures.
A mix between the static analysis of Pex with Korat’s capability to
generate useful data structures is the path we will follow.
The study of pre- pos conditions inference using static analysis
[Moy 2009] will be useful to infer OCL rules.
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space
15. Korat repOK method for LinkedList
1 public boolean repOK () {
2 if ( Head == null || Tail == null )
3 return size == 0;
4 if ( size == 1) return Head == Tail ;
5 if ( Head . Prev != null ) return false ;
6 if ( Tail . Next != null ) return false ;
7 LinkedListElement T last = Head ;
8 Set visited = new HashSet () ;
9 LinkedList workList = new LinkedList () ;
10 visited . add ( Head ) ;
11 workList . add ( Head ) ;
12 while (! workList . isEmpty () ) {
13 LinkedListElement T current = ( LinkedListElement T ) workList .
removeFirst () ;
14 if ( current . Next != null ) {
15 if (! visited . add ( current . Next ) )
16 return false ;
17 workList . add ( current . Next ) ;
18 if ( current . Next . Prev != current ) return false ;
19 last = current . Next ;
20 }
21 }
22 if ( last != Tail )
23 return false ;
24 return ( visited . size () == size ) ;
25 }
Ulisses Costa, Daniela da Cruz, Pedro Rangel Henriques Automatic Test Generation for Space