This set of slides was used for the tutorial given by Tudor Girba, Michele Lanza and Radu Marinescu at International Conference on Software Engineering (ICSE) 2008.
Modeling History to Understand Software Evolution With Hismo 2008-02-25 Tudor Girba
Over the past three decades, more and more research has been spent on understanding software evolution. However, the approaches developed so far rely on ad-hoc models, or on too specific meta-models, and thus, it is difficult to reuse or compare their results. We argue for the need of an explicit and generic meta-model that recognizes evolution as an explicit phenomenon and models it as a first class entity. Our solution is to encapsulate the evolution in the explicit notion of history as a sequence of versions, and to build a meta-model around these notions called Hismo. To show the usefulness of our meta-model we exercise its different characteristics by building several reverse engineering applications.
Modeling History to Understand Software Evolution With Hismo 2008-02-25 Tudor Girba
Over the past three decades, more and more research has been spent on understanding software evolution. However, the approaches developed so far rely on ad-hoc models, or on too specific meta-models, and thus, it is difficult to reuse or compare their results. We argue for the need of an explicit and generic meta-model that recognizes evolution as an explicit phenomenon and models it as a first class entity. Our solution is to encapsulate the evolution in the explicit notion of history as a sequence of versions, and to build a meta-model around these notions called Hismo. To show the usefulness of our meta-model we exercise its different characteristics by building several reverse engineering applications.
Modeling History to Understand Software Evolution with Hismo 2008-03-12Tudor Girba
Over the past three decades, more and more research has been spent on understanding software evolution. However, the approaches developed so far rely on ad-hoc models, or on too specific meta-models, and thus, it is difficult to reuse or compare their results. We argue for the need of an explicit and generic meta-model that recognizes evolution as an explicit phenomenon and models it as a first class entity. Our solution is to encapsulate the evolution in the explicit notion of history as a sequence of versions, and to build a meta-model around these notions called Hismo. To show the usefulness of our meta-model we exercise its different characteristics by building several reverse engineering applications.
Understanding software systems is hampered by their sheer size and complexity. Software visualization encodes the data found in these systems into pictures and enables the human eye to interpret it. In this lecture we present the concepts of software visualization and we show several examples of how visualizations can help in understanding software systems.
GT Spotter is a user interface that unifies the search workflow in an IDE. This set of slides was used for a submission at the ESUG 2015 Innovation Awards.
Modeling History to Understand Software Evolution with Hismo 2008-03-12Tudor Girba
Over the past three decades, more and more research has been spent on understanding software evolution. However, the approaches developed so far rely on ad-hoc models, or on too specific meta-models, and thus, it is difficult to reuse or compare their results. We argue for the need of an explicit and generic meta-model that recognizes evolution as an explicit phenomenon and models it as a first class entity. Our solution is to encapsulate the evolution in the explicit notion of history as a sequence of versions, and to build a meta-model around these notions called Hismo. To show the usefulness of our meta-model we exercise its different characteristics by building several reverse engineering applications.
Understanding software systems is hampered by their sheer size and complexity. Software visualization encodes the data found in these systems into pictures and enables the human eye to interpret it. In this lecture we present the concepts of software visualization and we show several examples of how visualizations can help in understanding software systems.
GT Spotter is a user interface that unifies the search workflow in an IDE. This set of slides was used for a submission at the ESUG 2015 Innovation Awards.
I watched 1800+ TED talks. I watched all those published on ted.com. Why? Because I am a TED addict. And because each of these talks reminds me that storytelling is essential in everything we do.
Facts are important, but facts alone have no value. They have to be consumed to worthwhile. Stories make this happen by getting us involved. This applies to researching novel ways, it applies to creating products, it applies to leading people, it applies to educating kids, and it applies to marriage proposals. Essentially, it applies to anything worth doing.
Storytelling is what makes stories happen. But, storytelling is a skill, and like any skill, it can be learnt.
For example, an easy way to learn is to listen to good examples. Like TED talks. But, there are many ways to learn. And, there are even more ways to apply.
It only takes us to invest in it. Why?
Because storytelling is essential.
Moose: how to solve real problems without reading codeTudor Girba
I use this set of slides for a talk I gave at ESUG 2014.
Abstract:
Moose is a platform for software and data analysis (http://moosetechnology.org). It runs on Pharo and it can help you figure out problems around software systems.
In this talk, I show several real-life examples of how custom tools built on top of Moose helped solve concrete problems. The examples vary both in scope and in the kind of problems. For example, we talk about how we fixed a caching problem in a Java system by analyzing logs, or how we fixed a Morphic problem by means of visualization and interaction. Even if these problems are so different, all of them were solvable with one uniform set of programmable tools.
That is the power of Moose, and it is now at the fingertips of any Pharo programmer.
We cannot continue to let systems loose in the wild without any concern for how we will deal with them at a later time. Two decades ago, Richard Gabriel coined the idea of software habitability. Indeed, given that engineers spend a significant part of their active life inside software systems, it is desirable for that system to be suitable for humans to live there.
We go further and introduce the concept of software environmentalism based on a simple principle: Engineers have the right to build upon assessable systems and have the responsibility of producing assessable systems.
The emergent nature of software systemsTudor Girba
This slideshow offers an argument for how the structure of a software system has an inherently emergent nature.
More information can be found at: http://humane-assessment.com
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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.
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/
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…
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.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
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
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.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
Pragmatic Design Quality Assessment - (Tutorial at ICSE 2008)
1. Pragmatic Design Quality Assessment
Tudor Gîrba
University of Bern, Switzerland
Michele Lanza
University of Lugano, Switzerland
Radu Marinescu
Politehnica University of Timisoara, Romania
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21. What is the current state?
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23. Reverse engineering is analyzing a subject system to:
identify components and their relationships, and
create more abstract representations.
Chikofky & Cross, 90
24. { {
{ {
}
}
}
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A large system contains lots of details.
25. ity?
its qual
ju dge
How to { {
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A large system contains lots of details.
34. Examples of size metrics
NOM - number of methods
NOA - number of attributes
LOC - number of lines of code
NOS - number of statements
NOC - number of children
Lorenz, Kidd, 1994
Chidamber, Kemerer, 1994
35. McCabe cyclomatic complexity (CYCLO) counts
the number of independent paths through the code of a
function.
McCabe, 1977
it reveals the minimum number of tests to write
interpretation can’t directly lead to improvement action
36. Weighted Method Count (WMC) sums up the
complexity of class’ methods (measured by the metric
of your choice; usually CYCLO).
Chidamber, Kemerer, 1994
it is configurable, thus adaptable to our precise needs
interpretation can’t directly lead to improvement action
37. Depth of Inheritance Tree (DIT) is the (maximum)
depth level of a class in a class hierarchy.
Chidamber, Kemerer, 1994
inheritance is measured
only the potential and not the real impact is quantified
38. Coupling between objects (CBO) shows the number
of classes from which methods or attributes are used.
Chidamber, Kemerer, 1994
it takes into account real dependencies not just declared ones
no differentiation of types and/or intensity of coupling
39. Tight Class Cohesion (TCC) counts the relative
number of method-pairs that access attributes of the
class in common.
Bieman, Kang, 1995
TCC = 2 / 10 = 0.2
interpretation can lead to improvement action
ratio values allow comparison between systems
45. Problem 1: metrics granularity
?
capture symptoms, not causes of problems
in isolation,
they don’t lead to improvement solutions
46. Problem 1: metrics granularity
?
capture symptoms, not causes of problems
in isolation,
they don’t lead to improvement solutions
Problem 2: implicit mapping
we don’t reason in terms of metrics,
but in terms of design principles
47. 2 big obstacles in using metrics:
Thresholds make metrics hard to interpret
Granularity make metrics hard to use in isolation
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51. fo
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53. etr ics!
w ith m
o do
i ng t
tn oth
I wan
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54. How to get an initial understanding of a system?
55. Metric Value
LOC 35175
NOM 3618
NOC 384
CYCLO 5579
NOP 19
CALLS 15128
FANOUT 8590
AHH 0.12
ANDC 0.31
56. Metric Value
LOC 35175
NOM 3618
NOC 384
CYCLO 5579
NOP 19
CALLS 15128
FANOUT 8590
AHH 0.12
ANDC 0.31
57. Metric Value
LOC 35175
NOM 3618
NOC 384
CYCLO 5579
NOP 19
CALLS 15128
FANOUT 8590
ha t?
ww
AHH
An d no 0.12
ANDC 0.31
66. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
ANDC 0.31
AHH 0.12
20.21 NOP 19
9.42 NOC 384
9.72 NOM 3618 NOM 418
0.15 LOC 35175 15128 CALLS 0.56
CYCLO 5579 8590 FANOUT
close to high close to average close to low
67. The Overview Pyramid provides a metrics
overview. Lanza, Marinescu 2006
close to high close to average close to low
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69. etr ics!
w ith m
o do
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77. God Classes tend to centralize the intelligence of the
system, to do everything and to use data from small
data-classes.
Riel, 1996
78. God Classes tend
to centralize the intelligence of the system,
to do everything and
to use data from small data-classes.
79. God Classes
centralize the intelligence of the system,
do everything and
use data from small data-classes.
80. God Classes
are complex,
are not cohesive,
access external data.
81. God Classes
are complex,
are not cohesive,
access external data.
sing
uer ies u
to q s
s in ator
etric per
se m ical o
Co mpo log
82. Detection Strategies are metric-based queries to
detect design flaws. Lanza, Marinescu 2006
Rule 1
METRIC 1 > Threshold 1
AND Quality problem
Rule 2
METRIC 2 < Threshold 2
83. Shotgun
Surgery has
uses is
has (partial) Feature Data
Envy uses Class
is partially
God has
Intensive Class
Coupling Brain has
has
Method
Extensive Brain has Significant
Coupling Class Duplication
has
is
is
has
Refused
is Tradition
Parent
Breaker
Bequest
has (subclass)
Futile
Hierarchy Lanza, Marinescu 2006
Identity Collaboration Classification
Disharmonies Disharmonies Disharmonies
84. A God Class centralizes too much intelligence in
the system. Lanza, Marinescu 2006
Class uses directly more than a
few attributes of other classes
ATFD > FEW
Functional complexity of the
class is very high
AND GodClass
WMC ! VERY HIGH
Class cohesion is low
TCC < ONE THIRD
85. An Envious Method is more interested in data
from a handful of classes. Lanza, Marinescu 2006
Method uses directly more than
a few attributes of other classes
ATFD > FEW
Method uses far more attributes
of other classes than its own
AND Feature Envy
LAA < ONE THIRD
The used quot;foreignquot; attributes
belong to very few other classes
FDP ! FEW
86. Data Classes are dumb data holders.
Lanza, Marinescu 2006
Interface of class reveals data
rather than offering services
WOC < ONE THIRD
AND Data Class
Class reveals many attributes and is
not complex
87. Data Classes are dumb data holders.
Lanza, Marinescu 2006
More than a few public
data
NOAP + NOAM > FEW
AND
Complexity of class is not
high
WMC < HIGH
Class reveals many
OR attributes and is not
Class has many public complex
data
NOAP + NOAM > MANY
AND
Complexity of class is not
very high
WMC < VERY HIGH
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116. We do not see with our eyes, but with our brain.
Our brain works like a computer, with 3 types of memory
Iconic memory, the visual sensory register
Short-term memory, the working memory
Long-term memory
Sensation Perception
(Physical Process) (Cognitive Process)
Stimulus Sensory Organ Perceptual Organ
Brain
Iconic Memory - Short-term Memory - Long-term Memory
118. Iconic Short-term
memory memory
< 1 second
very fast
automatic
subconscious
preattentive
119. Iconic Short-term
memory memory
< 1 second couple of seconds
very fast 3-9 chunks
automatic
subconscious
preattentive
120. Categorizing Preattentive Attributes
Category Form Color Spatial Motion Motion
Orientation Hue 2D position Flicker
Line length Intensity Direction
Line width
Size
Attribute
Shape
Curvature
Added marks
Enclosure
134. Software visualization is
the use of the crafts of typography, graphic design, animation,
and cinematography with modern human-computer interaction
and computer graphics technology to facilitate both the human
understanding and effective use of computer software.
Price, Becker, Small
183. ENOM LENOM EENOM
balanced changer 7 3.5 3.25
late changer 7 5.75 1.37
3 1 2
dead stable 0 0 0
early changer 7 1.25 5.25
184. ENOM LENOM EENOM
balanced changer 7 3.5 3.25
late changer 7 5.75 1.37
ed.
measur3 1 2
be
ry can
H isto
dead stable 0 0 0
early changer 7 1.25 5.25
185. History can be measured in many ways.
Evolution Number of Methods
Stability Number of Lines of Code
Historical Max of Cyclomatic Complexity
Growth Trend Number of Modules
... ...
186. The recently changed parts are likely to change in the
near future.
Common wisdom
187. The recently changed parts are likely to change in the
near future.
ally?
Common wisdom
re
re they
A
195. past future
YesterdayWeatherHit(present):
past:=histories.topLENOM(start, present)
future:=histories.topEENOM(present, end)
past.intersectWith(future).notEmpty()
present
196. past future
YesterdayWeatherHit(present):
past:=histories.topLENOM(start, present)
future:=histories.topEENOM(present, end)
past.intersectWith(future).notEmpty()
present
prediction hit
197. Yesterday’s Weather shows the localization of changed in
time. Girba etal, 2004
hit hit hit
YW = 3 / 8 = 37%
hit hit hit hit hit hit hit
YW = 7 / 8 = 87%
198. A God Class centralizes too much intelligence in
the system.
Class uses directly more than a
few attributes of other classes
ATFD > FEW
Functional complexity of the
class is very high
AND GodClass
WMC ! VERY HIGH
Class cohesion is low
TCC < ONE THIRD
199. A God Class centralizes too much intelligence in
the system.
Class uses directly more than a
few attributes of other classes
ATFD > FEW
tab le?
f it is s
wh
Functional complexity of the at i
ut,
class is very high
B AND GodClass
WMC ! VERY HIGH
Class cohesion is low
TCC < ONE THIRD
200. History-based Detection Strategies take evolution
into account. Ratiu etal, 2004
God Class
in the last version
isGodClass(last)
AND Harmless God Class
Stable throughout
the history
Stability > 90%
201. What happens with inheritance?
A A A A A
B C B C B C B B
D D D E
ver .1 ver. 2 ver. 3 ver. 4 ver. 5
A is persistent, B is stable, C was removed, E is newborn ...
202. Hierarchy Evolution encapsulates time.
Girba etal, 2005
A
changed
methods
changed
age
lines
C B
Removed
Removed
D E
A is persistent, B is stable, C was removed, E is newborn ...
222. Pragmatic Design Quality Assessment
Tudor Gîrba
University of Bern, Switzerland
Michele Lanza
University of Lugano, Switzerland
Radu Marinescu
Politehnica University of Timisoara, Romania
223. Tudor Gîrba, Michele Lanza, Radu Marinescu
http://creativecommons.org/licenses/by/3.0/