In a world of ever-changing business requirements, how can you keep your software moving at the same pace?
If you keep adding lines of code around the previous iteration to add new functionality, things can become complex quite fast.
By measuring complexity, you can resolve and prevent bugs, while measuring class responsibility can make refactoring easier, for example.
In this talk Dennis will go through certain concepts of analysing software with automated tools to spit out numbers which tell a story about your code.
Sometimes you see code that is perfectly OK according to the definition of the language, but which is flawed because it breaks too many established idioms and conventions. On the other hand, a solid piece of code is something that looks like it is written by an experienced person who cares about professionalism in programming.
A presentation at Norwegian Developer Conference 2010
Approaches and techniques for statically finding a multitude of issues in source code have been developed in the past. A core property of these approaches is that they are usually targeted towards finding only a very specific kind of issue and that the effort to develop such an analysis is significant. This strictly limits the number of kinds of issues that can be detected.
In this paper, we discuss a generic approach based on the detection of infeasible paths in code that can discover a wide range of code smells ranging from useless code that hinders comprehension to real bugs. Code issues are identified by calculating the difference between the control-flow graph that contains all technically possible edges and the corresponding graph recorded while performing a more precise analysis using abstract interpretation.
We have evaluated the approach using the Java Development Kit as well as the Qualitas Corpus (a curated collection of over 100 Java Applications) and were able to find thousands of issues across a wide range of categories.
Sometimes you see code that is perfectly OK according to the definition of the language, but which is flawed because it breaks too many established idioms and conventions. On the other hand, a solid piece of code is something that looks like it is written by an experienced person who cares about professionalism in programming.
A presentation at Norwegian Developer Conference 2010
Approaches and techniques for statically finding a multitude of issues in source code have been developed in the past. A core property of these approaches is that they are usually targeted towards finding only a very specific kind of issue and that the effort to develop such an analysis is significant. This strictly limits the number of kinds of issues that can be detected.
In this paper, we discuss a generic approach based on the detection of infeasible paths in code that can discover a wide range of code smells ranging from useless code that hinders comprehension to real bugs. Code issues are identified by calculating the difference between the control-flow graph that contains all technically possible edges and the corresponding graph recorded while performing a more precise analysis using abstract interpretation.
We have evaluated the approach using the Java Development Kit as well as the Qualitas Corpus (a curated collection of over 100 Java Applications) and were able to find thousands of issues across a wide range of categories.
An introductory PowerPoint to understanding and applying the C++ programming language. Broken down into lessons covering:
Introduction: Definitions and the History of Programming
Lesson 1: Libraries, Variables and Types, Blocks and Namespaces, Functions
Lesson 2: If...Else Statements, For and While Loops, Scope
Exercise: Program a Gumball Game
Lesson 3: Classes and Objects, Pointers
Programming is hard. Programming correct C and C++ is particularly hard. Indeed, both in C and certainly in C++, it is uncommon to see a screenful containing only well defined and conforming code.Why do professional programmers write code like this? Because most programmers do not have a deep understanding of the language they are using.While they sometimes know that certain things are undefined or unspecified, they often do not know why it is so. In these slides we will study small code snippets in C and C++, and use them to discuss the fundamental building blocks, limitations and underlying design philosophies of these wonderful but dangerous programming languages.
This content has a CC license. Feel free to use it for whatever you want. You may download the original PDF file from: http://www.pvv.org/~oma/DeepC_slides_oct2012.pdf
Let's turn the table. Suppose your goal is to deliberately create buggy programs in C and C++ with serious security vulnerabilities that can be "easily" exploited. Then you need to know about things like stack smashing, shellcode, arc injection, return-oriented programming. You also need to know about annoying protection mechanisms such as address space layout randomization, stack canaries, data execution prevention, and more. These slides will teach you the basics of how to deliberately write insecure programs in C and C++.
A PDF version of the slides can be downloaded from my homepage: http://olvemaudal.com/talks
Here is a video recording of me presenting these slides at NDC 2014: http://vimeo.com/channels/ndc2014/97505677
Enjoy!
The presentation shows major features of the new C++ standard (language and the library). The full list of new things is very broad, so I've categorized them to be easier to understand.
Programming is an essential skill if you seek a career in software development, or in other fields of Coding. This fundamental of programming course is the first in the specialization for Introduction to Programming in C, but its lessons extend to any language you might want to study. This is because programming is primarily about solving a set of problems and writing the algorithm.
An introductory PowerPoint to understanding and applying the C++ programming language. Broken down into lessons covering:
Introduction: Definitions and the History of Programming
Lesson 1: Libraries, Variables and Types, Blocks and Namespaces, Functions
Lesson 2: If...Else Statements, For and While Loops, Scope
Exercise: Program a Gumball Game
Lesson 3: Classes and Objects, Pointers
Programming is hard. Programming correct C and C++ is particularly hard. Indeed, both in C and certainly in C++, it is uncommon to see a screenful containing only well defined and conforming code.Why do professional programmers write code like this? Because most programmers do not have a deep understanding of the language they are using.While they sometimes know that certain things are undefined or unspecified, they often do not know why it is so. In these slides we will study small code snippets in C and C++, and use them to discuss the fundamental building blocks, limitations and underlying design philosophies of these wonderful but dangerous programming languages.
This content has a CC license. Feel free to use it for whatever you want. You may download the original PDF file from: http://www.pvv.org/~oma/DeepC_slides_oct2012.pdf
Let's turn the table. Suppose your goal is to deliberately create buggy programs in C and C++ with serious security vulnerabilities that can be "easily" exploited. Then you need to know about things like stack smashing, shellcode, arc injection, return-oriented programming. You also need to know about annoying protection mechanisms such as address space layout randomization, stack canaries, data execution prevention, and more. These slides will teach you the basics of how to deliberately write insecure programs in C and C++.
A PDF version of the slides can be downloaded from my homepage: http://olvemaudal.com/talks
Here is a video recording of me presenting these slides at NDC 2014: http://vimeo.com/channels/ndc2014/97505677
Enjoy!
The presentation shows major features of the new C++ standard (language and the library). The full list of new things is very broad, so I've categorized them to be easier to understand.
Programming is an essential skill if you seek a career in software development, or in other fields of Coding. This fundamental of programming course is the first in the specialization for Introduction to Programming in C, but its lessons extend to any language you might want to study. This is because programming is primarily about solving a set of problems and writing the algorithm.
Using cyclomatic complexity to measure code complexityJane Chung
To make sure your code is maintainable and programmer-friendly, you should use CCN to analyze your code complexity.
--
Brought to you by Skygear.io -- an open source development kit for mobile, web & IoT apps.
Find us on Twitter @Oursky
Any non-trivial site or application on the Web today will contain a large amount of HTML, CSS, and JavaScript. As the use of the Internet evolves and our dependence on it increases, having a plan for organizing and maintaining your front-end code is an absolute must.
Every company has legacy applications.
And every framework, language, technology will dead or will become legacy.
How to modernise them without the need to rewrite everything by hand?
Does an automatic migration of source code is realistic on large applications?
Does Eclipse technologies help for these tasks?
This talk will answer these questions through a real use case executed for a french ministry: the migration of a large scale application (5 millions of line of code) from Forte to Java.
We will explain our project process to insure 99,9999% of garanties to migrated code and how we create a custom migration factory with:
EMF to represent source code as a structured model
CDO to store several Gigabits of datas with very good performances
Agility to reverse existing source code
GMF and ATL for Software Mining and cartography
Acceleo for translating
The talk will also show demos of this migration tooling applied to other technologies and other needs of legacy analysis:
ADA to C++
VB to DotNet
OracleForms to JavaEE
Natural Sonar (quality checker)
Cobol cartography
This talk introduces some code metrics concepts with easy examples, and how to obtain them in PHP using tools like phploc, phpdepend, phpmetrics, etc. It follows with a discussion on which metrics are relevant, how we are already using them in some projects and how we should evaluate them in our department.
It is quite often that software developers have absolutely no clue about the cost of an error. It is very important that the error be found at the earliest possible stage.
Performs code analysis in C, C++, C++/CLI, C++/CX, C#. Plugin for Visual Studio 2010-2015. Integration with SonarQube, QtCreator, CLion, Eclipse CDT, Anjuta DevStudio and so on. Standalone utility. Direct integration of the analyzer into the systems of build automation and the BlameNotifier utility (e-mail notification). Automatic analysis of modified files. Great scalability. Why do people need code analyzers?
It's quite hard to write cross-platform CPAN modules, especially when you use XS to interface with C libraries. Luckily, CPAN Testers tests your modules on many platforms for you. Come see how CPAN Testers helped me to create a fully portable module.
Presented at YAPC::Europe 2011.
100 bugs in Open Source C/C++ projects Andrey Karpov
This article demonstrates capabilities of the static code analysis methodology. The readers are offered to study the samples of one hundred errors found in open-source projects in C/C++.
Using static code analysis tools and detecting and fixing identified issues is very important in order to improve the quality and security of the code baseline.
CodeChecker (https://github.com/Ericsson/codechecker ) is an open source analyzer tooling, defect database and viewer extension for the Clang Static Analyzer and Clang Tidy.
It provides a number of additional features:
- Good visualization of problems in the code
- Overview of results for the whole product
- Filtering
- Cross translational unit analysis and statistical checkers support
- Suppression handling
- And many others...
These features simplify the follow up of results and make it more efficient.
In the video, an overview of features and capabilities of CodeChecker is demonstrated as well as a description and recommendation of how to introduce new tools.
Recording of the demo: https://youtu.be/sQ2Qj0kHoRY published in C++ Dublin User group https://www.youtube.com/channel/UCZ4UNE_1IMUFfAhcdq7CMOg/
Useful links:
open source project: https://github.com/Ericsson/codechecker
http://codechecker-demo.eastus.cloudapp.azure.com/login.html#
demo/demo
https://codechecker.readthedocs.io/en/latest/
http://clang-analyzer.llvm.org/available_checks.html
http://clang.llvm.org/extra/clang-tidy/checks/list.html
Other related videos about Clang Static Analyzer and CodeChecker that goes a bit more deeply into how Clang Static Analyzer works:
Clang Static Analysis - Meeting C++ 2016 Gabor Horvath
https://www.youtube.com/watch?v=UcxF6CVueDM
CppCon 2016: Gabor Horvath “Make Friends with the Clang Static Analysis Tools"
https://www.youtube.com/watch?v=AQF6hjLKsnM
▪ Developed a recursive-descent parser to generate an intermediate representation for subsequent optimizations in Java
▪ Implemented common subexpression elimination and copy propagation on control flow graph
▪ Deployed a code generator for the source language that yields optimized native programs
This article demonstrates capabilities of the static code analysis methodology. The readers are offered to study the samples of one hundred errors found in open-source projects in C/C++. All the errors have been found with the PVS-Studio static code analyzer.
One of the biggest problems of software projects is that, while the practice of software development is commonly thought of as engineering, it is inherently a creative discipline; hence, many things about it are hard to measure. While simple yardsticks like test coverage and cyclomatic complexity are important for code quality, what other metrics can we apply to answer questions about our code? What coding conventions or development practices can we implement to make our code easier to measure? We'll take a tour through some processes and tools you can implement to begin improving code quality in your team or organization, and see what a difference it makes to long-term project maintainability. More importantly, we'll look at how we can move beyond today's tools to answer higher-level questions of code quality. Can 'good code' be quantified?
One of the biggest problems of software projects is that, while the practice of software development is commonly thought of as engineering, it is inherently a creative discipline; hence, many things about it are hard to measure. While simple yardsticks like test coverage and cyclomatic complexity are important for code quality, what other metrics can we apply to answer questions about our code? What coding conventions or development practices can we implement to make our code easier to measure? We'll take a tour through some processes and tools you can implement to begin improving code quality in your team or organization, and see what a difference it makes to long-term project maintainability. More importantly, we'll look at how we can move beyond today's tools to answer higher-level questions of code quality. Can 'good code' be quantified?
"Why is there no artificial intelligence yet?" Or, analysis of CNTK tool kit ...PVS-Studio
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10. LOC: Physical lines of code
LLOC: Logical lines of code
CLOC: Comment lines of code
NCLOC: Non-comment lines of code
Different types:
11. How many lines of code?
7 physical lines of code
3 logical lines of code
1 comment lines of code
6 non-comment lines of code
1 <?php
2
3 /* this is a for-loop */
4 for ($i = 0; $i < 100; $i++)
5 {
6 printf("hello");
7 }
12. How many lines of code?
6 physical lines of code
3 logical lines of code
3 comment lines of code
3 non-comment lines of code
1 <?php
2
3 /**
4 * This is a for-loop
5 */
6 for ($i = 0; $i < 100; $i++) printf("hello");
14. WARNING!
Don’t use these statistics as a form
of measuring quality or productivity!
People will just ‘game’ the system
*applies different codestyle*
15.
16. Use it to gain more insight
about the application
“Measuring programming progress by lines of code
is like measuring aircraft building progress by weight.”
--Bill Gates
17. composer require phploc/phploc
$ phploc loc1.php
phploc 2.0.2 by Sebastian Bergmann.
!
Size
Lines of Code (LOC) 7
Comment Lines of Code (CLOC) 1 (14.29%)
Non-Comment Lines of Code (NCLOC) 6 (85.71%)
Logical Lines of Code (LLOC) 3 (42.86%)
Classes 0 (0.00%)
Average Class Length 0
Average Method Length 0
Functions 0 (0.00%)
Average Function Length 0
Not in classes or functions 3 (100.00%)
19. Cyclomatic Complexity
“It is a quantitative measure of the number of
linearly independent paths through a program's source code.”
M = E − N + 2P
E = the number of edges of the graph.
N = the number of nodes of the graph.
P = the number of connected components.
http://www.literateprogramming.com/mccabe.pdf
27. N-path Complexity
“The NPath complexity of a method is the number of
acyclic execution paths through that method.”
28. “The NPath complexity of a method is the number of
acyclic execution paths through that method.”
N-path Complexity
29. “The NPath complexity of a method is the number of
acyclic execution paths through that method.”
N-path Complexity
30. N-path Complexity
Path coverage
M = E − N + 2P
E = the number of edges of the graph.
N = the number of nodes of the graph.
P = the number of connected components.
38. Measuring Coupling
Afferent Coupling (Ca):"
Amount of other packages depending on this class (incoming)
!
Efferent Coupling (Ce):"
Amount of dependencies of this class
!
Instability (I):"
Resilience to change, metric between 0 and 1. Where 0 is stable.
I = Ce / (Ce + Ca)
39. Measuring Coupling
Ca = 2
Ce = 3
I = Ce / (Ce + Ca)
I = 3 / (3+2)
I = 0,6
I = 0,0 = Completely stable
I = 1,0 = Completely unstable
40. Measuring Coupling
Abstract:"
Either an abstract class or interface
!
Concrete:"
Any class that is neither abstract or final
!
Abstractness (A):"
How abstract a package is, ratio between 0 and 1. Where 0 is concrete.
A = Abstract / (Abstract + Concrete)
41. Measuring Coupling
PHPMD (PHP Mess Detector)
Can detect (code size rules):
- Cyclomatic complexity
- N-path complexity
- Excessive method length
- Too many methods
- etcetera
10
200
100
10
And other things
- Clean Code
- Design Rules
- Naming Rules
- Unused Code Rules