This document studied the impact of dependency network measures on software quality. It found that incorporating social network analysis (SNA) measures into bug prediction models improved recall and precision by 25% over models using traditional software metrics alone. Specifically, local network measures like cluster fan-in and layer bypass had the most significant impact. The models were evaluated on the Eclipse project and showed potential to prioritize testing efforts and reduce costs.
Wer sich professionell mit der Publikation wissenschaftlicher Erträge beschäftigt, weiß, was die Titel von Beiträgen in der Wissenschaftskommunikation alles leisten müssen. Doch welche Regeln der Kommunikation sollten die Macher zudem beachten? Warum lohnt es sich, die Tugenden der Titelei bei Tageszeitungs- und Zeitschriftenjournalisten abzuschauen? Wie schafft man es, einen Titel über ein wissenschaftliches Thema gleichermaßen seriös wie aufmerksamkeitsweckend zu formulieren? Und welche Werkzeuge können Wissenschaftskommunikatoren anwenden, um
schnell und sicher im Schreiballtag zu einem treffenden Titel zu kommen?
Feature Extraction for Predictive LTV Modeling using Hadoop, Hive, and Cascad...Kontagent
Description:
One of the biggest challenges for people building data products today is developing and refining features for modeling purposes (i.e. feature extraction) with the volume and variability of web scale data. In this talk, Martin will discuss some of the challenges and solutions faced by Kontagent as it built out a predictive lifetime value model for its customers. As you will learn, Hadoop is critical to this feature extraction process, and Cascading is quite handy when building out more complex features than can be readily developed in a query framework like Hive.
Speaker:
Martin Colaco, Director of Data Science for Kontagent
Wer sich professionell mit der Publikation wissenschaftlicher Erträge beschäftigt, weiß, was die Titel von Beiträgen in der Wissenschaftskommunikation alles leisten müssen. Doch welche Regeln der Kommunikation sollten die Macher zudem beachten? Warum lohnt es sich, die Tugenden der Titelei bei Tageszeitungs- und Zeitschriftenjournalisten abzuschauen? Wie schafft man es, einen Titel über ein wissenschaftliches Thema gleichermaßen seriös wie aufmerksamkeitsweckend zu formulieren? Und welche Werkzeuge können Wissenschaftskommunikatoren anwenden, um
schnell und sicher im Schreiballtag zu einem treffenden Titel zu kommen?
Feature Extraction for Predictive LTV Modeling using Hadoop, Hive, and Cascad...Kontagent
Description:
One of the biggest challenges for people building data products today is developing and refining features for modeling purposes (i.e. feature extraction) with the volume and variability of web scale data. In this talk, Martin will discuss some of the challenges and solutions faced by Kontagent as it built out a predictive lifetime value model for its customers. As you will learn, Hadoop is critical to this feature extraction process, and Cascading is quite handy when building out more complex features than can be readily developed in a query framework like Hive.
Speaker:
Martin Colaco, Director of Data Science for Kontagent
Estimating the principal of Technical Debt - Dr. Bill Curtis - WTD '12OnTechnicalDebt
This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors.
This presentaiton on Overall Equipment Effectiveness, Down Time Analytics and Assett Utilization was developed by me and a coleague during my tenure at ISS. Presentation was given to the Chattanooga, TN Chapter of the SME.
Estimating the principal of Technical Debt - Dr. Bill Curtis - WTD '12OnTechnicalDebt
This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors.
This presentaiton on Overall Equipment Effectiveness, Down Time Analytics and Assett Utilization was developed by me and a coleague during my tenure at ISS. Presentation was given to the Chattanooga, TN Chapter of the SME.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Studying the impact of dependency network measures on software quality
1. Studying
the
impact
of
dependency
network
measures
on
soIware
quality
Thanh
H.
D.
Nguyen,
Bram
Adams,
Ahmed
E.
Hassan
SAIL,
School
of
Compu?ng,
Queen’s
University,
Kingston,
Canada
3. Bug
predic?on
models
Bug
Predic5on
Model
High
Recall
-‐>
We
won’t
miss
a
possible
bug
High
Precision
-‐>
We
won’t
waste
effort
3
4. SoIware
is
more
than
just
size
and
complexity
Node" A
D
C
Local
Neighborhood" B
F
Global
Neighborhood" E
G
4
5. SoIware
is
more
than
just
size
and
complexity
Traditional Metrics
Node"
(MET)"
Local
Neighborhood" Social Network
Measures!
Global (SNA)"
Neighborhood"
5
6. Bug
Predic5on
Model
Would
SNA
improve
performance?
6
16. Which
metrics
provide
the
improvement?
Node" 12
Metrics
Local
11
Metrics
Neighborhood"
Global
Neighborhood" 12
Metrics
Use
hierarchical
modeling
to
find
important
group
[Caltado
et
al.
TSE10]
16
17. Which
metrics
provide
the
improvement?
Node" 12
Metrics
7%
Local
11
Metrics
+2.7%
Neighborhood"
Global
Neighborhood" 12
Metrics
+0.3%
17
18. Which
metrics
provide
the
improvement?
Node" 12
Metrics
7%
Local
11
Metrics
+2.7%
Neighborhood"
Global
Neighborhood" 12
Metrics
+0.3%
Local
neighbours
have
most
of
the
important
improvement
18
28. Comparing
Performance
Using
Effort
Aware
Curves
100
80
File
A
B
C
% bugs caught
#bug
0
1
2
60
LOC
48
8
44
40
ROI
0
0.125
0.045
20
Risk
0.78
0.56
0.34
0
0 20 40 60 80 100
% lines of code reviewed
28
29. Comparing
Performance
Using
Effort
Aware
Curves
100
80
File
A
B
C
% bugs caught
#bug
0
1
2
60
LOC
48
8
44
40 A
ROI
0
0.125
0.045
20
Risk
0.78
0.56
0.34
0
0 20 40 60 80 100
% lines of code reviewed
29
30. Comparing
Performance
Using
Effort
Aware
Curves
100
80
File
A
B
C
% bugs caught
#bug
0
1
2
60
LOC
48
8
44
40
ROI
0
0.125
0.045
20
B
Risk
0.78
0.56
0.34
0
0 20 40 60 80 100
% lines of code reviewed
30
31. Comparing
Performance
Using
Effort
Aware
Curves
100
80
File
A
B
C
% bugs caught
#bug
0
1
2
60
LOC
48
8
44
40 C
ROI
0
0.125
0.045
20
Risk
0.78
0.56
0.34
0
0 20 40 60 80 100
% lines of code reviewed
31
32. Is
this
a
good
predic?on?
100
80
File
A
B
C
% bugs caught
#bug
0
1
2
60
LOC
48
8
44
40
ROI
0
0.125
0.045
20
Risk
0.78
0.56
0.34
0
0 20 40 60 80 100
% lines of code reviewed
32
33. Beeer
predic?on
means
a
higher
curve
100
Good
80
File
A
B
C
% bugs caught
#bug
0
1
2
60
LOC
48
8
44
40
ROI
0
0.125
0.045
Bad
20
Bad
0.78
0.56
0.34
Good
0.32
0.72
0.55
0
0 20 40 60 80 100
% lines of code reviewed
33
34. The
predic?on
model
helps
reduce
tes?ng
effort
100
Random
File
80
% bugs caught
60
File
40
Package
20
0
0 20 40 60 80 100
% lines of code reviewed 34