An introductory lecture on Context-Oriented Programming, part of the course LINGI2252 “Software Maintenance and Evolution”, given by Prof. Kim Mens at UCL, Belgium. This particular lecture was made by Dr. Sebastian Gonzalez in close collaboration with Prof. Kim Mens.
An introductory lecture on Context-Oriented Programming, part of the course LINGI2252 “Software Maintenance and Evolution”, given by Prof. Kim Mens at UCL, Belgium. This particular lecture was made by Dr. Sebastian Gonzalez in close collaboration with Prof. Kim Mens.
Software testing is an investigation conducted to provide stakeholders with information about the quality of the product or service under test.
Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation.
Why is Software Testing Important to a business?
Software testing is a process to determine the quality of the software developed by a developer or programmer. It is a methodological study intended to evaluate the quality-related information of the product. Understanding of the important features and advantages of software testing helps businesses in their day-to-day activities.
Testing can be done in two ways, manual testing and automated testing. Manual software testing is done by human testers, who manually check the code and report bugs in it. In case of automated testing, testing is performed by a computer using software such as WinRunner, LoadRunner, etc.
Refactoring for Software Design Smells Book - A Visual OverviewGanesh Samarthyam
Check out this presentation that provides a visual overview of our book "Refactoring for Software Design Smells Book: Managing Technical Debt" (Morgan Kaufmann/Elsevier, 2014, with translation available in Korean). URL: www.designsmells.com
Software testing is an investigation conducted to provide stakeholders with information about the quality of the product or service under test.
Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation.
Why is Software Testing Important to a business?
Software testing is a process to determine the quality of the software developed by a developer or programmer. It is a methodological study intended to evaluate the quality-related information of the product. Understanding of the important features and advantages of software testing helps businesses in their day-to-day activities.
Testing can be done in two ways, manual testing and automated testing. Manual software testing is done by human testers, who manually check the code and report bugs in it. In case of automated testing, testing is performed by a computer using software such as WinRunner, LoadRunner, etc.
Refactoring for Software Design Smells Book - A Visual OverviewGanesh Samarthyam
Check out this presentation that provides a visual overview of our book "Refactoring for Software Design Smells Book: Managing Technical Debt" (Morgan Kaufmann/Elsevier, 2014, with translation available in Korean). URL: www.designsmells.com
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Recently I fell by chance on the Periodic Table of the Elements... Long time no see... Remembering my physics lessons in University, I always loved that table. I remembered spending hours understanding the layout and admiring the beauty of its natural simplicity.
So I had the idea of trying the same layout, not the same approach since both are not comparable, really only the same layout for Agile Principles and Practices.
The result is in this presentation: The Periodic Table of Agile Principles and Practices:
This is take two of the presentation, some things added, some removed, but still the regurgitation is best..
The purpose is to raise your awareness of software architecture in light of modern day agile development. Disciplines to incorporate and reconsider
Presentation introducing LISP, looking at the history and concepts behind this powerfull programming language.
Presentation by Tijs van der Storm for the sept 2012 Devnology meetup at the Mirabeau offices in Amsterdam
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• Communication Mining Overview
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• How can it help today’s business and the benefits
• Phases in Communication Mining
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Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
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Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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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:
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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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.
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* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
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…
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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.
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
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
4. How often did you ...
... encounter greenfield and non-greenfield software
engineering?
4
5. Why non-greenfield engineering?
Because existing software, often called legacy software, is
valuable
Often business-critical
A huge amount of money has already been invested in it
Has been tested and runs
Does (mainly) what it should do
Would you replace such a system?
5
7. Lehman’s Laws of software evolution
Continuing change
A program that is used in a real-world environment must change, or
become progressively less useful in that environment.
Increasing complexity
As a program evolves, it becomes more complex, and extra resources are
needed to preserve and simplify its structure.
For more information read Lehman and Belady, 1985
7
9. Lehman’s Laws in practice
Existing software Is often modified in an ad-hoc manner (quick
fixes)
Lack of time, resources, money, etc.
Initial good design is not maintained
Spaghetti code, copy/paste programming, dependencies are introduced,
no tests, etc.
Documentation is not updated (if there is one)
Architecture and design documents
Original developers leave and with them their knowledge
9
11. Implications of the results
Software maintenance costs continuously increase
Between 50% and 75% of global software development costs
are spent on maintenance!
Up to 60% of a maintenance effort is spent on understanding
the existing software
11
12. What is your decision?
According to Lehman: “there will always be changes”
hack it?
* duplicated code * first reengineer
* complex conditionals * then implement changes
* abusive inheritance
* large classes/methods
Take a loan on your software Investment for the future
pay back via reengineering paid back during maintenance
12
14. Let’s reengineer
Definition:
“Reengineering is the examination and alteration of a subject
system to reconstitute it in a new form and the subsequent
implementation of the new form.”
[Demeyer, Ducasse, Nierstrasz]
http://scg.unibe.ch/download/oorp/
14
15. Reengineering Life-Cycle
(1) requirement New
analysis Requirements
(3) problem
detection (4) problem
resolution
Designs
(2) model
capture
Code
15
17. Goals of reengineering (2)
Unbundling
Split a monolithic system into parts that can be separately marketed
Performance
“First do it, then do it right, then do it fast”
Design extraction
To improve maintainability, portability, etc.
Exploitation of New Technology
I.e., new language features, standards, libraries, etc.
17
18. In this course, you will learn
Best practices to analyze and understand software systems
(i.e., reverse engineering)
Heuristics and tools to detect shortcomings in the design and
implementation of software systems
18
20. Setting direction patterns
Set direction
Where to start?
Agree on Maxims
Maintain Coordinate
direction direction
Most Valuable First
Appoint a Speak to the
Navigator Round Table What to do? What not to do?
Fix Problems, If It Ain't Broke
Not Symptoms Don't Fix It
Principles & guidelines for
software project management are
How to do it?
especially relevant for reengineering
projects
Keep it Simple
20
22. Most valuable first (2)
Solution: Work on aspects that are most valuable to your
customer
Maximize commitment
Deliver results early
Build confidence
22
24. Most valuable first (4)
How do you tell what is valuable?
Identify your customer
Understand the customer’s business model
Determine measurable goals
Consult change logs for high activity
Play the Planning Game
Fix Problems, not Symptoms
24
27. What is Reverse Engineering and why?
Reverse Engineering is the process of analyzing a subject system
to identify the system’s components and their interrelationships and
create representations of the system in another form or at a higher level
of abstraction [Chikofsky & Cross, ’90]
Motivation
Understanding other people’s code, the design and architecture in order
to maintain and evolve a software system
27
28. First contact patterns
feasibility assessment
System experts (one week time)
Talk with Talk with
developers end users
Chat with the Interview Talk about it
Maintainers during Demo
Software System
Verify what
you hear Read about
Read it Compile it
it
Read All the Code Skim the Do a Mock
in One Hour Documentation Installation
28
29. Pattern: Read all the code in one hour
Problem: Yes, but… the system is so big! Where to start?
29
30. Read all the code in one hour (2)
Solution: Read the code in one hour
Focus on:
Functional tests and unit tests
Abstract classes and methods and classes high in the hierarchy
Surprisingly large structures
Comments
Check classes with high fan-out
Study the build process
30
31. In Java programs focus on
public abstract class Example { public interface IExample {
... ...
} }
/**
public class Test {
* Block comment
...
*/
@Test
public class Example {
public void testExample() {
public void foo() {
...
int x = 1;
}
for (int x=1; i<100; i++) {
}
// do something comment
}
}
}
31
32. First project plan
Project scope (1/2 page)
Description, context, goals, verification criteria
Opportunities
Identify factors to achieve project goals
Skilled maintainers, readable source-code, documentation, etc.
Risks
Identify risks that may cause problems
Absent test-suites, missing libraries, etc.
Record likelihood & impact for each risk
Go/no-go decision, activities (fish-eye view)
32
34. Initial understanding patterns
Top down
Recover
design
Speculate about Design
ITERATION
understand ⇒
higher-level model
Analyze the Study the
Persistent Data Exceptional Entities
Recover Identify
database problems
Bottom up
34
35. Study the exceptional entities
Problem: How can you quickly identify design problems?
Solution: Measure software entities and study the anomalous ones
Visualize metrics to get an overview
Use simple metrics
Lines of code
Number of methods
...
35
36. Example: Exceptional entities
Use simple
Use simple
metrics and
metrics and
layout
layout
algorithms
algorithms.
height colour
(x,y) width
Visualize up
to 5 metrics
per node
36
38. Detailed model capture patterns
Tie Code and Questions Expose the design & make sure it
stays exposed
Expose design
Keep track of
your understanding Refactor to Understand
Expose collaborations
Step through the Execution
Expose contracts Write Tests
to Understand
Look for the Contracts
• Use Your Tools
• Look for Key Methods Expose evolution
• Look for Constructor Calls
• Look for Template/Hook Methods Learn from the Past
• Look for Super Calls
38
39. Refactor to understand
Problem: How do you decipher cryptic code?
Solution: Refactor it till it makes sense
Goal (for now) is to understand, not to reengineer
Hints
Work with a copy of the code
Refactoring requires an adequate test base
If this is missing, “Write Tests to Understand”
39
40. Refactor to understand (cont.)
Guidelines
Rename attributes to convey roles
Rename methods and classes to reveal intent
Remove duplicated code
Replace condition branches by methods
40
41. Learn from the past
Problem: How did the system get the way it is? Which parts are
stable and which aren’t?
Solution: Compare versions to discover where code was removed
Removed functionality is a sign of design evolution
Use or develop appropriate tools
Look for signs of:
Unstable design — repeated growth and refactoring
Mature design — growth, refactoring, and stability
41
42. Examples: Unstable design
Pulsar: Repeated Modifications make it grow and shrink.
System Hotspot: Every System Version requires changes.
42
47. Summary Model Capture
Setting direction patterns to
Set the goals
Find the Go/No-Go decision
Increase commitment of clients and developers
First contact patterns to
Obtain an overview and grasp the main issues
Assess the feasibility of the project
Initial Understanding & Detailed Model Capture
Plan the work … and work the plan
Frequent and short iterations
47
49. Design problems
The most common design problems result from code that is
Unclear & complicated Duplicated (code clones)
49
50. Code Smells (if it stinks, change it)
A code smell is a hint that something has gone wrong
somewhere in your code.
Duplicated Code
Long Method
Large Class
Long Parameter List
Divergent Change
Shotgun Surgery
Feature Envy
...
50
51. How to detect?
Measure and visualize quality aspects of the current
implementation of a system
Source code metrics and structures
Measure and visualize quality aspects of the evolution of a
system
Evolution metrics and structures
Use Polymetric Views
51
52. Polymetric Views
A combination of metrics and software
visualization Entity
Visualize software using colored rectangles for
the entities and edges for the relationships Relationship
Render up to five metrics on one node:
Size (1+2)
Color (3)
Position (4+5)
X Coordinate
Y Coordinate
Color tone
Height
Width
7 52
53. Smell 1: Long Method
The longer a method is, the more difficult it is to understand
it.
When is a method too long?
Heuristic: > 10 LOCs (?)
How to detect?
Visualize LOC metric values of methods
“Method Length Distribution View”
53
55. Smell 2: Switch Statement
Problem is similar to code duplication
Switch statement is scattered in different places
How to detect?
Visualize McCabe Cyclomatic Complexity metric to detect complex
methods
“Method Complexity Distribution View”
55
57. More info on Detection Strategies
Object-Oriented Metrics in Practice
Michele Lanza and Radu Marinescu, Springer 2006
http://www.springer.com/computer/swe/book/
978-3-540-24429-5
57
58. Tool for Smell Detection
inCode
http://www.intooitus.com/inCode.html
jDeodorant
http://java.uom.gr/~jdeodorant/
58
60. Understanding Evolution
Changes can point to design problems
“Evolutionary Smells”
But
Overwhelming complexity
How can we detect and understand changes?
Solutions
The Evolution Matrix
The Kiviat Graphs
60
61. Visualizing Class Evolution
Visualize classes as rectangles using for
width and height the following metrics: Foo
NOM (number of methods)
NOA (number of attributes) Bar
The Classes can be categorized according
to their “personal evolution” and to their
“system evolution”
-> Evolution Patterns
61
62. The Evolution Matrix
Removed Classes Last Version
First Version
Added
Classes
Major Leap
Growth Stabilisation
TIME (Versions)
62
64. Persistent / Dayfly
Dayflies: Exists
during only one or
two versions. Perhaps
Persistent: Has the same an idea which was
lifespan as the whole tried out and then
system. Part of the dropped.
original design. Perhaps
holy dead code which no
one dares to remove.
64
65. Pulsar / Supernova
Pulsar: Repeated Modifications make it grow and shrink.
System Hotspot: Every System Version requires changes.
Supernova: Sudden increase in size. Possible Reasons:
• Massive shift of functionality towards a class.
• Data holder class for which it is easy to grow.
• Sleeper: Developers knew exactly what to fill in.
65
66. White Dwarf / Red Giant / Idle
White Dwarf: Lost the functionality it had and now trundles along without
real meaning. Possibly dead code -> Lazy Class.
Red Giant: A permanent god (large) class which is always very large.
Idle: Keeps size over several versions. Possibly dead code,
possibly good code.
66
67. Summary Problem Detection
Design Problems
Result from duplicated, unclear, complicated source code
-> Code Smells
Detection heuristics and Polymetric Views to detect code and
evolution smells
67
68. Conclusions
Object-Oriented Re-engineering Patterns
Set of best practices to re-engineering software systems
Module Capture and Reverse Engineering
Understand the design and implementation of software systems
Problem Detection
Heuristics to detect Bad Smells in the source code and evolution of
software systems
Next Step
Add tests and refactor detected problems
68
69. Reading material
Object-Oriented Reengineering Patterns
Serge Demeyer, Stephane Ducasse, and Oscar Nierstrasz
free copy from: http://scg.unibe.ch/download/oorp/
Working Effectively with Legacy Code
Michael Feathers, Prentice Hall, 1 edition, 2004
Refactoring to Patterns
Joshua Kerievsky, Addison-Wesley Professional, 2004
69
70. Additional reading
Agile Software Development: Principles Patterns, and Practices
Robert C. Martin, Prentice Hall
Object-Oriented Design Heuristics
Arthur J. Riel, Prentice Hall, 1 edition, 1996
Refactoring: Improving the Design of Existing Code
Martin Fowler, Addison-Wesley Professional, 1999
70