Slide related to the paper: "Machine learning based code smell detection through WekaNose", available at:
- http://essere.disco.unimib.it/wiki/wekanose (Project website)
- https://doi.org/10.1145/3183440.3194974 (DOI)
Dokumen ini membahas tentang ikatan kimia yang terbagi menjadi tiga jenis yaitu ikatan ion, ikatan logam, dan ikatan kovalen. Ikatan ion terbentuk antara logam dan nonlogam, ikatan logam antara logam-logam, sedangkan ikatan kovalen antara nonlogam-nonlogam. Ikatan kovalen dibedakan berdasarkan jumlah pasangan elektron yang digunakan bersama dan asal pasangan elektron tersebut.
The s-block elements constitute Groups IA (the alkali metals) and IIA (the alkaline earth metals) of the periodic table. These elements share several key properties:
- They are highly reactive metals and strong reducing agents.
- Alkali metals (Group IA) exhibit a +1 oxidation state and form ionic compounds by losing their single outer electron. Their physical properties include being soft and silvery-white metals that are good conductors of heat and electricity. They also demonstrate trends in atomic size, ionization energy, and reactivity down their respective groups.
- Important characteristics include standard electrode potential, flame tests, solubility in liquid ammonia, and decreasing hydration energy with increasing
The document discusses lambda expressions in Java 8. It provides background on the lambda calculus and functional programming. Lambda expressions allow anonymous functions and are implemented using functional interfaces in Java 8. This enables a more functional style of programming. Lambda expressions can access variables from their enclosing scope and method references provide a concise way to pass existing methods. The streams API allows functional-style operations on collections and supports sequential and parallel processing.
Multi step automated refactoring for code smelleSAT Journals
Abstract
Brain MR Image can detect many abnormalities like tumor, cysts, bleeding, infection etc. Analysis of brain MRI using image
processing techniques has been an active research in the field of medical imaging. In this work, it is shown that MR image of brain
represent a multi fractal system which is described a continuous spectrum of exponents rather than a single exponent (fractal
dimension). Multi fractal analysis has been performed on number of images from OASIS database are analyzed. The properties of
multi fractal spectrum of a system have been exploited to prove the results. Multi fractal spectra are determined using the modified
box-counting method of fractal dimension estimation.
Keywords: Brain MR Image, Multi fractal, Box-counting
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Introduction to Machine Learning for newcomers. It will show you some basic concepts like what is supervised learning, unsupervised learning, classification, regression, under/overfitting, clustering, anomaly detection, and how to have some measures. It will illustrates examples through scikit-learn and tensorflow code
Recommending Software Refactoring Using Search-based Software EnginneringAli Ouni
This document discusses recommending software refactoring using search-based software engineering. It proposes a three-part approach: 1) using genetic programming to generate rules for detecting code smells, 2) applying mono-objective search algorithms like genetic algorithms to recommend refactorings to address code smells, and 3) using a multi-objective algorithm like NSGA-II to recommend refactorings that optimize multiple objectives like quality metrics and design patterns. The approach is evaluated on several systems, achieving over 90% precision on average in detecting three types of code smells.
Combinatorial testing can significantly reduce the number of tests needed to cover all variable combinations by focusing on pairwise combinations. The document discusses pairwise testing, which aims to test all combinations of each pair of input parameters. This catches a high percentage of errors while dramatically reducing the number of required test cases. Tools like PICT can automatically generate optimal pairwise test suites. The document provides an example showing PICT reducing 96 potential test cases for a car ordering application down to just 8 test cases.
Dokumen ini membahas tentang ikatan kimia yang terbagi menjadi tiga jenis yaitu ikatan ion, ikatan logam, dan ikatan kovalen. Ikatan ion terbentuk antara logam dan nonlogam, ikatan logam antara logam-logam, sedangkan ikatan kovalen antara nonlogam-nonlogam. Ikatan kovalen dibedakan berdasarkan jumlah pasangan elektron yang digunakan bersama dan asal pasangan elektron tersebut.
The s-block elements constitute Groups IA (the alkali metals) and IIA (the alkaline earth metals) of the periodic table. These elements share several key properties:
- They are highly reactive metals and strong reducing agents.
- Alkali metals (Group IA) exhibit a +1 oxidation state and form ionic compounds by losing their single outer electron. Their physical properties include being soft and silvery-white metals that are good conductors of heat and electricity. They also demonstrate trends in atomic size, ionization energy, and reactivity down their respective groups.
- Important characteristics include standard electrode potential, flame tests, solubility in liquid ammonia, and decreasing hydration energy with increasing
The document discusses lambda expressions in Java 8. It provides background on the lambda calculus and functional programming. Lambda expressions allow anonymous functions and are implemented using functional interfaces in Java 8. This enables a more functional style of programming. Lambda expressions can access variables from their enclosing scope and method references provide a concise way to pass existing methods. The streams API allows functional-style operations on collections and supports sequential and parallel processing.
Multi step automated refactoring for code smelleSAT Journals
Abstract
Brain MR Image can detect many abnormalities like tumor, cysts, bleeding, infection etc. Analysis of brain MRI using image
processing techniques has been an active research in the field of medical imaging. In this work, it is shown that MR image of brain
represent a multi fractal system which is described a continuous spectrum of exponents rather than a single exponent (fractal
dimension). Multi fractal analysis has been performed on number of images from OASIS database are analyzed. The properties of
multi fractal spectrum of a system have been exploited to prove the results. Multi fractal spectra are determined using the modified
box-counting method of fractal dimension estimation.
Keywords: Brain MR Image, Multi fractal, Box-counting
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Introduction to Machine Learning for newcomers. It will show you some basic concepts like what is supervised learning, unsupervised learning, classification, regression, under/overfitting, clustering, anomaly detection, and how to have some measures. It will illustrates examples through scikit-learn and tensorflow code
Recommending Software Refactoring Using Search-based Software EnginneringAli Ouni
This document discusses recommending software refactoring using search-based software engineering. It proposes a three-part approach: 1) using genetic programming to generate rules for detecting code smells, 2) applying mono-objective search algorithms like genetic algorithms to recommend refactorings to address code smells, and 3) using a multi-objective algorithm like NSGA-II to recommend refactorings that optimize multiple objectives like quality metrics and design patterns. The approach is evaluated on several systems, achieving over 90% precision on average in detecting three types of code smells.
Combinatorial testing can significantly reduce the number of tests needed to cover all variable combinations by focusing on pairwise combinations. The document discusses pairwise testing, which aims to test all combinations of each pair of input parameters. This catches a high percentage of errors while dramatically reducing the number of required test cases. Tools like PICT can automatically generate optimal pairwise test suites. The document provides an example showing PICT reducing 96 potential test cases for a car ordering application down to just 8 test cases.
What's the Difference Between Static Analysis and Compiler Warnings?Andrey Karpov
Visiting forums, you can often meet people who believe that compiler warnings are more than enough for controlling the basic types of errors in programs' code. I want to show you that it's a wrong belief.
Specialized static analysis tools and standard compiler warnings are both oriented towards improving the quality of the source code and minimizing potential bugs which are difficult to catch through debugging. One way or another, the compiler relies on static analysis of the source code during compilation to generate its warnings, but the quality of diagnostics and their scope of use vary greatly from one analysis method to another.
AGRipppin is a novel search based testing technique for Android Applications aimed at generating test suites that are more effective than the ones obtained by the Model-Learning
This document discusses requirements traceability and testing capabilities in Simulink. It describes how Simulink can trace requirements bidirectionally between models and source code. It also outlines how Simulink Design Verifier can automatically generate tests to achieve high coverage and check designs against requirements. Finally, it positions several MathWorks products in the V&V process and highlights key capabilities of Simulink Test for authoring, managing and executing simulation-based tests.
Automation test is an interesting research problem in recent years. There are many reasons why we use automation test in the software development. In the traditional approach, automation test has been used for regression test, functional test, performance test… in order to find or prevent bugs and software quality assurance. In this research, we have a novel approach using automation test to build software monitoring solution. The purpose of automation scripts use as monitoring software to capture images and write logs. The architecture pattern of automation for monitoring based on automation test tool, cloud service, and scheduler. The proposed architecture pattern has been applied for online advertisement monitoring. Instead of reporting passed/failed, automation scripts will monitor whether the advertisement is display or not, and how often it was display on multiple platforms. The proposed architecture pattern can also apply for video advertisement monitoring solution.
Diving into the World of Test Automation The Approach and the TechnologiesQASymphony
This presentation was originally given at Quality Jam London. Elise covered test automation and the progression for test automation that you might encounter. The session agenda included:
The stages of the test team
Why are we automating?
What are we automating?
How are we automating?
What languages should we use?
What frameworks and libraries should we use?
Open source or proprietary?
Learn more at www.qualityjam.com
Automated Software Testing Framework Training by Quontra SolutionsQuontra Solutions
Learn through Experience -- We differentiate our training and development program by delivering Role-Based training instead of Product-based training. Ultimately, our goal is to deliver the best IT Training to our clients.
In this training, attendees learn:
Introduction to Automation
• What is automation
• Advantages of automation & Disadvantages of automation
• Different types of Automation Tools
• What to automate in projects
• When to start automation. Scope for automation testing in projects
• About open-source automation tools
Introduction to Selenium
• What is selenium
• Why selenium
• Advantage and Disadvantages of selenium
Selenium components
• Selenium IDE
• Selenium RC
• Selenium WebDriver
• Selenium Grid
Selenium IDE
• Introduction to IDE
• IDE Installation
• Installation and uses of Firepath, Firebug & Debug bar
• Property & value of elements
• Selenium commands
• Assertions & Verification
• Running, pausing and debugging script
• Disadvantages of selenium IDE
• How to convert selenium IDE Scripts into other languages
Locators
• Tools to identify elements/objects
• Firebug
• IE Developer tools
• Google Chrome Developer tools
• Locating elements by ID
• Finding elements by name
• Finding elements by link text
• Finding elements by XPath
• Finding Elements by using CSS
• Summary
Selenium RC
• What is selenium RC
• Advantages of RC, Architecture
• What is Eclipse/IntelliJ, Selenium RC configure with Eclipse/IntelliJ
• Creating, running & debugging RC scripts
Java Concepts
• Introduction to OOPs concepts and Java
• Installation: Java, Eclipse/IntelliJ, selenium, TestNg/JUnit
• operators in java
• Data types in java
• Conditional statements in java
• Looping statements in java
• Output statements in java
• Classes & Objects
• Collection Framework
• Regular Expressions
• Exception Handling
• Packages, Access Specifiers /Modifiers
• String handling
• Log4J for logging
Selenium Web Driver with Java
• Introduction to WebDriver
• Advantages
• Different between RC and WebDriver
• Selenium WebDriver- commands
• Generate scripts in Eclipse/IntelliJ. Run Test Scripts.
• Debugging Test Script
• Database Connections
• Assertions, validations
• Working with Excel
• Pass the data from Excel
• Working with multiple browser
• Window Handling, Alert/confirm & Popup Handling
• Mouse events
• Wait mechanism
• Rich Web Handling: Calendar handing, Auto suggest, Ajax, browser forward/back navigation, keyboard events, certificate handling, event listeners
TestNg/JUnit Framework
• What is TestNg/JUnit
• Integrate the Selenium Scripts and Run from TestNg/JUnit
• Reporting Results and Analysis
• Run Scripts from multiple programs
• Parallel running using TestNg/JUnit
Automation Framework development in Agile testing
• Introduction to Frame W
Driving Risks Out of Embedded Automotive SoftwareParasoft
Automobiles are becoming the ultimate mobile computer. Popular models have as many as 100 Electronic Control Units (ECUs), while high-end models push 200 ECUs. Those processors run hundreds of millions of lines of code written by the OEMs’ teams and external contractors—often for black-box assemblies. Modern cars also have increasingly sophisticated high-bandwidth internal networks and unprecedented external connectivity. Considering that no code is 100% error-free, these factors point to an unprecedented need to manage the risks of failure—including protecting life and property, avoiding costly recalls, and reducing the risk of ruinous lawsuits.
GPCE16: Automatic Non-functional Testing of Code Generators FamiliesMohamed BOUSSAA
The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate high-level system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators.
Machine Learning in Static Analysis of Program Source CodeAndrey Karpov
Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we'll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities.
Lesson 7. The issues of detecting 64-bit errorsPVS-Studio
There are various techniques of detecting errors in program code. Let us consider the most popular ones and see how efficient they are in finding 64-bit errors.
Achieving quality with tools case studyEosSoftware
The document discusses various techniques for achieving quality in software development, including static analysis tools. It describes how bugs occur due to human errors and complex codebases, and that testing alone cannot find all bugs. It then explains static analysis tools like FindBugs and PMD that can automatically find bugs, the challenges of static analysis, distinguishing bugs from style issues, and how tools like Eclipse and EclEmma are used to fix bugs and measure test coverage. The overall goal is to reduce bugs escaping to production through efficient tooling and quality practices.
The document discusses the key activities involved in system implementation: coding, testing, installation, documentation, training, and support. It describes the processes of coding, testing, and installation. Testing involves various types of tests, including static/dynamic, automated/manual, inspection, integration, system, and acceptance testing. Installation strategies like direct, parallel, single-location, and phased installation are outlined. The importance of documentation, training, and supporting users is also highlighted.
Deepcoder to Self-Code with Machine LearningIRJET Journal
The document discusses DeepCoder, a machine learning system developed by Microsoft that is able to generate its own code by learning from existing code examples. DeepCoder is trained on a large corpus of programs and input/output examples to learn which code snippets are likely to work together to solve new problems. It can then search through code more efficiently than humans to assemble working programs from existing code blocks. While currently limited to simple 5 line programs, DeepCoder represents a significant improvement over previous program synthesis techniques and could eventually make programming accessible to non-coders. However, some media reports exaggerated DeepCoder's capabilities and inaccurately claimed it works by copying code directly from other software.
Regular use of static code analysis in team developmentPVS-Studio
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
Regular use of static code analysis in team developmentAndrey Karpov
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
What's the Difference Between Static Analysis and Compiler Warnings?Andrey Karpov
Visiting forums, you can often meet people who believe that compiler warnings are more than enough for controlling the basic types of errors in programs' code. I want to show you that it's a wrong belief.
Specialized static analysis tools and standard compiler warnings are both oriented towards improving the quality of the source code and minimizing potential bugs which are difficult to catch through debugging. One way or another, the compiler relies on static analysis of the source code during compilation to generate its warnings, but the quality of diagnostics and their scope of use vary greatly from one analysis method to another.
AGRipppin is a novel search based testing technique for Android Applications aimed at generating test suites that are more effective than the ones obtained by the Model-Learning
This document discusses requirements traceability and testing capabilities in Simulink. It describes how Simulink can trace requirements bidirectionally between models and source code. It also outlines how Simulink Design Verifier can automatically generate tests to achieve high coverage and check designs against requirements. Finally, it positions several MathWorks products in the V&V process and highlights key capabilities of Simulink Test for authoring, managing and executing simulation-based tests.
Automation test is an interesting research problem in recent years. There are many reasons why we use automation test in the software development. In the traditional approach, automation test has been used for regression test, functional test, performance test… in order to find or prevent bugs and software quality assurance. In this research, we have a novel approach using automation test to build software monitoring solution. The purpose of automation scripts use as monitoring software to capture images and write logs. The architecture pattern of automation for monitoring based on automation test tool, cloud service, and scheduler. The proposed architecture pattern has been applied for online advertisement monitoring. Instead of reporting passed/failed, automation scripts will monitor whether the advertisement is display or not, and how often it was display on multiple platforms. The proposed architecture pattern can also apply for video advertisement monitoring solution.
Diving into the World of Test Automation The Approach and the TechnologiesQASymphony
This presentation was originally given at Quality Jam London. Elise covered test automation and the progression for test automation that you might encounter. The session agenda included:
The stages of the test team
Why are we automating?
What are we automating?
How are we automating?
What languages should we use?
What frameworks and libraries should we use?
Open source or proprietary?
Learn more at www.qualityjam.com
Automated Software Testing Framework Training by Quontra SolutionsQuontra Solutions
Learn through Experience -- We differentiate our training and development program by delivering Role-Based training instead of Product-based training. Ultimately, our goal is to deliver the best IT Training to our clients.
In this training, attendees learn:
Introduction to Automation
• What is automation
• Advantages of automation & Disadvantages of automation
• Different types of Automation Tools
• What to automate in projects
• When to start automation. Scope for automation testing in projects
• About open-source automation tools
Introduction to Selenium
• What is selenium
• Why selenium
• Advantage and Disadvantages of selenium
Selenium components
• Selenium IDE
• Selenium RC
• Selenium WebDriver
• Selenium Grid
Selenium IDE
• Introduction to IDE
• IDE Installation
• Installation and uses of Firepath, Firebug & Debug bar
• Property & value of elements
• Selenium commands
• Assertions & Verification
• Running, pausing and debugging script
• Disadvantages of selenium IDE
• How to convert selenium IDE Scripts into other languages
Locators
• Tools to identify elements/objects
• Firebug
• IE Developer tools
• Google Chrome Developer tools
• Locating elements by ID
• Finding elements by name
• Finding elements by link text
• Finding elements by XPath
• Finding Elements by using CSS
• Summary
Selenium RC
• What is selenium RC
• Advantages of RC, Architecture
• What is Eclipse/IntelliJ, Selenium RC configure with Eclipse/IntelliJ
• Creating, running & debugging RC scripts
Java Concepts
• Introduction to OOPs concepts and Java
• Installation: Java, Eclipse/IntelliJ, selenium, TestNg/JUnit
• operators in java
• Data types in java
• Conditional statements in java
• Looping statements in java
• Output statements in java
• Classes & Objects
• Collection Framework
• Regular Expressions
• Exception Handling
• Packages, Access Specifiers /Modifiers
• String handling
• Log4J for logging
Selenium Web Driver with Java
• Introduction to WebDriver
• Advantages
• Different between RC and WebDriver
• Selenium WebDriver- commands
• Generate scripts in Eclipse/IntelliJ. Run Test Scripts.
• Debugging Test Script
• Database Connections
• Assertions, validations
• Working with Excel
• Pass the data from Excel
• Working with multiple browser
• Window Handling, Alert/confirm & Popup Handling
• Mouse events
• Wait mechanism
• Rich Web Handling: Calendar handing, Auto suggest, Ajax, browser forward/back navigation, keyboard events, certificate handling, event listeners
TestNg/JUnit Framework
• What is TestNg/JUnit
• Integrate the Selenium Scripts and Run from TestNg/JUnit
• Reporting Results and Analysis
• Run Scripts from multiple programs
• Parallel running using TestNg/JUnit
Automation Framework development in Agile testing
• Introduction to Frame W
Driving Risks Out of Embedded Automotive SoftwareParasoft
Automobiles are becoming the ultimate mobile computer. Popular models have as many as 100 Electronic Control Units (ECUs), while high-end models push 200 ECUs. Those processors run hundreds of millions of lines of code written by the OEMs’ teams and external contractors—often for black-box assemblies. Modern cars also have increasingly sophisticated high-bandwidth internal networks and unprecedented external connectivity. Considering that no code is 100% error-free, these factors point to an unprecedented need to manage the risks of failure—including protecting life and property, avoiding costly recalls, and reducing the risk of ruinous lawsuits.
GPCE16: Automatic Non-functional Testing of Code Generators FamiliesMohamed BOUSSAA
The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate high-level system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators.
Machine Learning in Static Analysis of Program Source CodeAndrey Karpov
Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we'll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities.
Lesson 7. The issues of detecting 64-bit errorsPVS-Studio
There are various techniques of detecting errors in program code. Let us consider the most popular ones and see how efficient they are in finding 64-bit errors.
Achieving quality with tools case studyEosSoftware
The document discusses various techniques for achieving quality in software development, including static analysis tools. It describes how bugs occur due to human errors and complex codebases, and that testing alone cannot find all bugs. It then explains static analysis tools like FindBugs and PMD that can automatically find bugs, the challenges of static analysis, distinguishing bugs from style issues, and how tools like Eclipse and EclEmma are used to fix bugs and measure test coverage. The overall goal is to reduce bugs escaping to production through efficient tooling and quality practices.
The document discusses the key activities involved in system implementation: coding, testing, installation, documentation, training, and support. It describes the processes of coding, testing, and installation. Testing involves various types of tests, including static/dynamic, automated/manual, inspection, integration, system, and acceptance testing. Installation strategies like direct, parallel, single-location, and phased installation are outlined. The importance of documentation, training, and supporting users is also highlighted.
Deepcoder to Self-Code with Machine LearningIRJET Journal
The document discusses DeepCoder, a machine learning system developed by Microsoft that is able to generate its own code by learning from existing code examples. DeepCoder is trained on a large corpus of programs and input/output examples to learn which code snippets are likely to work together to solve new problems. It can then search through code more efficiently than humans to assemble working programs from existing code blocks. While currently limited to simple 5 line programs, DeepCoder represents a significant improvement over previous program synthesis techniques and could eventually make programming accessible to non-coders. However, some media reports exaggerated DeepCoder's capabilities and inaccurately claimed it works by copying code directly from other software.
Regular use of static code analysis in team developmentPVS-Studio
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
Regular use of static code analysis in team developmentAndrey Karpov
Static code analysis technologies are used in companies with mature software development processes. However, there might be different levels of using and introducing code analysis tools into a development process: from manual launch of an analyzer "from time to time" or when searching for hard-to-find errors to everyday automatic launch or launch of a tool when adding new source code into the version control system.
The article discusses different levels of using static code analysis technologies in team development and shows how to "move" the process from one level to another. The article refers to the PVS-Studio code analyzer developed by the authors as an example.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, 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.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
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1. Machine Learning based
Code Smell detection through
WekaNose
A research accomplished by:
ESSeRE Lab - University of Milan Bicocca
2. What is a Code Smell?
A code smell is a surface indication that usually corresponds to a deeper problem in the system.
More specifically:
● A code smell has to be sniffable (something that's quick to spot);
● A code smell don't always indicate a problem.
1 Machine Learning based Code Smell detection through WekaNose September 2018
3. What is the problem with the state of art
approaches?
● Code smells can be subjectively interpreted;
● The results provided by detectors are usually different;
● The agreement in the results is scarce;
The metrics and
thresholds selection
problems
2 Machine Learning based Code Smell detection through WekaNose September 2018
4. Why should we bother?
3 Machine Learning based Code Smell detection through WekaNose September 2018
5. ● This approach allows to exploits the full interpretability of Code Smells;
● This approach requires just to describe the Code Smell, rather than formalize a definition;
● The Machine Learning algorithm learn the concept from data;
Why should we use Machine Learning algorithms?
4 Machine Learning based Code Smell detection through WekaNose September 2018
6. What is WekaNose?
WekaNose is a tool that supports a workflow that
aims to train Machine Learning algorithms specifically
for Code Smell detection.
This whole process is divided in three main part:
● the creation of the dataset;
● the training and testing of the
Machine Learning algorithms;
● the evaluation of the Machine Learning
algorithms performance, in term of Code Smell
detection;
5 Machine Learning based Code Smell detection through WekaNose September 2018
7. Main problems
6 Machine Learning based Code Smell detection through WekaNose September 2018
● Creation of a balance dataset, in terms of:
○ Labels;
○ Statistical properties;
● Machine Learning Algorithms selection:
○ No free lunch theorem
● Do high performances in the Machine Learning context imply
high performance in the actual Code Smells detection?
8. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
Umberto Azadi, Francesca Arcelli Fontana, and Marco Zanoni. 2018. Machine learning based code smell detection through WekaNose. In Proceedings of the 40th International Conference on
Software Engineering: Companion Proceeedings (ICSE '18). ACM, New York, NY, USA, 288-289. DOI: https://doi.org/10.1145/3183440.3194974
7 Machine Learning based Code Smell detection through WekaNose September 2018
9. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Sampling and Label of
the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
Data Class:
The Data Class Code Smell refers to classes that store data without using complex functionality,
and having other classes that strongly rely on them. A Data Class reveals many attributes, it is not
complex, and it provides data field through accessor methods.
Switch Statement:
The Switch Statements code smell refers to method that contain a complex switch operator or a
sequence of if statements that compromise the readability or/and the clarity of the code.
8 Machine Learning based Code Smell detection through WekaNose September 2018
10. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
An example is the Qualitas Corpus:
the Qualitas Corpus is a curated collection of 112 software systems
intended to be used for empirical studies of code artefacts.
9 Machine Learning based Code Smell detection through WekaNose September 2018
11. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
● A large set of object-oriented metrics
at method, class and package levels
have been taken into account and they
are considered as independent
variables in our machine learning
approach
● All metrics have been computed
through “Design Features and Metrics
for Java” (DFMC4J) [1]
[1] http://essere.disco.unimib.it/wiki/jcodeodor_doc
10 Machine Learning based Code Smell detection through WekaNose September 2018
12. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
An Advisor is a deterministic rule, implemented locally or in an external
tool, that gives a classification of a code element (method or class), telling
if it is a code smell or not.
11 Machine Learning based Code Smell detection through WekaNose September 2018
13. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
12 Machine Learning based Code Smell detection through WekaNose September 2018
14. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
5 Machine Learning algorithms have
been considered so far:
● J48 (x3)
● Random Forest
● Naïve Bayes
● JRip
● SVM (x10)
Each algorithm have been trained
with and without the boosting
technique: AdaBoostM1.
Therefore 32 algorithms have been
trained, tested and compared.
13 Machine Learning based Code Smell detection through WekaNose September 2018
15. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
The parameters are considered the best if the performances of
the machine learning algorithms are maximised by them.
WEKA provides a set of algorithms that perform a greed
search with this purpose, that can be used in WekaNose.
Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining:
Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
14 Machine Learning based Code Smell detection through WekaNose September 2018
16. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
15 Machine Learning based Code Smell detection through WekaNose September 2018
The WEKA Experimenter [1] has been used to compare the
Machine Learning algorithms, specifically:
● A 10-fold cross-validation tests with 10 repetitions were
performed for each classifiers with the best parameters
found;
● The performances were compared using the corrected
paired t-tests.
Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data
Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.
17. WekaNose’s workflow
Describe the
Code Smell
Select a collection of
heterogeneous system
Extract code metrics
from all the systems
Use Code Smell advisors
to sample candidates
Label the instances
Choose the Machine
Learning algorithms
Perform the Machine Learning
parameter optimization
Compare the Machine Learning
algorithms with each other
Use the SonarQube Plug-in for
actually detect the Code Smell
16 Machine Learning based Code Smell detection through WekaNose September 2018
Sonar-WekaNose-Plugin is a SonarQube plugin
that is able to analyze Java code in order to notify
the presence of Code Smells by using the
Machine Learning algorithms trained through
WekaNose.
Alessandro Polastri (2018), Bachelor Thesis: “SonarQube plugin for Code Smell
detection through machine learning techniques”.
18. Results so far obtained: Algorithms Comparison
[1] Arcelli Fontana, Francesca & Mäntylä, Mika & Zanoni, Marco & Marino, Alessandro. (2015). Comparing and experimenting machine learning techniques for code smell detection. Empirical
Software Engineering. 21. DOI: https://doi.org/10.1007/s10664-015-9378-4;
[2] Umberto Azadi (2017), Bachelor Thesis: “Code smell detection through machine learning techniques”.
17 Machine Learning based Code Smell detection through WekaNose September 2018
19. Best
Algorithm
per
Code Smell
Code Smell Machine Learning Algorithm
Data Class Boosted J48 pruned
God Class Naïve Bayes
Feature Envy Boosted JRip
Long Method Boosted J48 Unpruned
Long Parameter List Boosted J48 Unpruned
Switch Statements JRip
18 Machine Learning based Code Smell detection through WekaNose September 2018
20. Threats to validity
● Threats to internal validity:
○ The manual evaluation of code smells is subject to certain degree error that concern the
developer’s experience, the knowledge and the ability to understand design issues and other
factors.
● Threats to external validity:
○ Code Smell candidates were selected with random sampling and stratifying the choice according
to the number of positive results of code smell Advisors. This criterion increases the probability of
selecting instances affected by code smell but the sampling method could cause a distortion during
the building of the training set, because the selection criterion is only partly random.
● Experiments limitations:
○ The selected systems are all open source;
○ The metrics used are only the one computed by DFMC4J
19 Machine Learning based Code Smell detection through WekaNose September 2018
21. Future Development
● Evaluate the severity (not just the presence) of the Code Smell;
● Evaluation of Active Learning techniques in this context;
● Social platforms to collect data;
● Train new machine learning algorithms in order to increase the number of identified code smells;
● Understand the correlation between the performance measures used to evaluate the machine
learning algorithms and the performance measures used to estimate the goodness of a code
smell detector;
● Design and evaluate hybrid rules that use both the machine learning algorithms and the rules set
by the user to perform a detection;
● Improve the comparison between machine learning-based and rule-based code smell detection:
○ Evaluating if there are algorithms that tend to guarantee high performance compared to actual detection,
by experimenting with comparisons on large software systems and by experimenting with the comparison
on software systems belonging to a specific application domain;
○ Extend the comparison by considering other code smells detection tools, based on different techniques;
20 Machine Learning based Code Smell detection through WekaNose September 2018
22. Thank you for your attention
http://essere.disco.unimib.it/wiki/wekanose