This is the presentation I gave during ITAKE unconference 2015 about SonarQube and Technical Debt.
http://2015.itakeunconf.com/sessions/patroklos-papapetrou-holding-down-your-technical-debt-with-sonarqube/
This document discusses code quality and the software analysis tool SonarQube. It begins with an introduction to the author and an overview of topics to be covered, including code quality principles, measuring quality, and technical debt. It then demonstrates how SonarQube can help development teams continuously measure and improve their code quality across seven key dimensions. The document concludes by emphasizing the importance of ongoing code quality management.
SonarQube is an open source tool that helps manage code quality. It covers 7 axes of code quality and allows for continuous inspection of code bases through its plugins. It provides benefits like reducing bugs and technical debt by detecting issues early. Key metrics reported include potential bugs, duplicates, and test coverage. The tool integrates with development workflows and provides visual dashboards and reports to track quality over time.
Sonar is a software quality management platform that enables developers to access and track code analysis data ranging from styling errors and potential bugs to code defects, duplications, lack of test coverage, and excess complexity. It supports over 20 programming languages. Some key features include over 600 coding rules, standard software metrics, the ability to drill down to source code details, a time machine feature to analyze technical debt and code smells over time, security measures, an extensible plugin system, and integrations with tools like Jenkins. Sonar also covers 7 axes of code quality and has an architecture that allows for analysis of code in a continuous integration workflow.
Continuous Inspection of Code Quality: SonarQubeEmre Dündar
SonarQube is an open source platform for continuous inspection of code quality. It uses static code analysis to generate software metrics and detect issues like bugs, vulnerabilities, and code smells. These issues are tracked over time to help developers fix problems early when they are cheap to address. SonarQube integrates with development tools and pipelines to perform analysis on commits and reject code that does not meet quality standards. This provides continuous feedback on code quality and helps enforce good development practices across teams.
SonarQube is an open source platform for continuously monitoring code quality. It detects bugs, vulnerabilities, and code smells based on configured quality rules and profiles. Projects can define quality gates that check thresholds for metrics like issues, coverage, and duplications to flag the quality status as passed, warning, or failed. SonarQube allows comparing metrics across versions to track quality over time and supports code processes like nightly and pull request analysis. A community provides support through rule and profile updates, new plugins, and biweekly meetings to discuss quality concerns.
SonarQube - Should I Stay or Should I Go ? Geeks Anonymes
...by Jérémie Fays, 3 june 2015.
Ever considered monitoring your code quality ? SonarQube is certainly a good candidate for that, and an open source one ! This presentation explains shortly the metrics you can track using SonarQube, and how it has been implemented at the University of Liege TTO.
This document summarizes Sonar, an open source platform for managing code quality. Sonar aggregates various tools to analyze code quality metrics like static analysis, duplicate code detection, and code coverage. It provides a web-based portal with visualizations of code quality statistics, violations, and technical debt over time. The document encourages trying out Sonar to improve code quality and discusses prioritizing areas to start like increasing test coverage or removing duplicate code.
This document discusses code quality and the software analysis tool SonarQube. It begins with an introduction to the author and an overview of topics to be covered, including code quality principles, measuring quality, and technical debt. It then demonstrates how SonarQube can help development teams continuously measure and improve their code quality across seven key dimensions. The document concludes by emphasizing the importance of ongoing code quality management.
SonarQube is an open source tool that helps manage code quality. It covers 7 axes of code quality and allows for continuous inspection of code bases through its plugins. It provides benefits like reducing bugs and technical debt by detecting issues early. Key metrics reported include potential bugs, duplicates, and test coverage. The tool integrates with development workflows and provides visual dashboards and reports to track quality over time.
Sonar is a software quality management platform that enables developers to access and track code analysis data ranging from styling errors and potential bugs to code defects, duplications, lack of test coverage, and excess complexity. It supports over 20 programming languages. Some key features include over 600 coding rules, standard software metrics, the ability to drill down to source code details, a time machine feature to analyze technical debt and code smells over time, security measures, an extensible plugin system, and integrations with tools like Jenkins. Sonar also covers 7 axes of code quality and has an architecture that allows for analysis of code in a continuous integration workflow.
Continuous Inspection of Code Quality: SonarQubeEmre Dündar
SonarQube is an open source platform for continuous inspection of code quality. It uses static code analysis to generate software metrics and detect issues like bugs, vulnerabilities, and code smells. These issues are tracked over time to help developers fix problems early when they are cheap to address. SonarQube integrates with development tools and pipelines to perform analysis on commits and reject code that does not meet quality standards. This provides continuous feedback on code quality and helps enforce good development practices across teams.
SonarQube is an open source platform for continuously monitoring code quality. It detects bugs, vulnerabilities, and code smells based on configured quality rules and profiles. Projects can define quality gates that check thresholds for metrics like issues, coverage, and duplications to flag the quality status as passed, warning, or failed. SonarQube allows comparing metrics across versions to track quality over time and supports code processes like nightly and pull request analysis. A community provides support through rule and profile updates, new plugins, and biweekly meetings to discuss quality concerns.
SonarQube - Should I Stay or Should I Go ? Geeks Anonymes
...by Jérémie Fays, 3 june 2015.
Ever considered monitoring your code quality ? SonarQube is certainly a good candidate for that, and an open source one ! This presentation explains shortly the metrics you can track using SonarQube, and how it has been implemented at the University of Liege TTO.
This document summarizes Sonar, an open source platform for managing code quality. Sonar aggregates various tools to analyze code quality metrics like static analysis, duplicate code detection, and code coverage. It provides a web-based portal with visualizations of code quality statistics, violations, and technical debt over time. The document encourages trying out Sonar to improve code quality and discusses prioritizing areas to start like increasing test coverage or removing duplicate code.
Sonarqube is an open source platform for continuously inspecting code quality. It uses tools like Checkstyle, PMD and FindBugs to evaluate code. Sonarqube can be run locally or using Docker. The document provides instructions for installing Sonarqube on Docker and running a Maven project through the Sonarqube analysis to check code quality.
This document provides an overview of SonarQube, a code quality management platform. It discusses how SonarQube can be used to track technical debt by aggregating and trending code metrics over time. The presentation includes a live demo and discusses integrating SonarQube into development workflows as well as future plans to add more Puppet support and checks.
It is not to complicated to keep new project with good code quality for half year. Maybe, for one year. But what if team works on some project for years? Or even ”better”: you need to support and grow large project after another team. Presentation describes Continuous Inspection, main measures of code quality that will make your life better, continuous inspection and how to cook it with SonarQube.
This document discusses SonarQube and the seven deadly sins of software development it helps identify. It begins by introducing SonarQube and its role in separating developers from code quality issues. It then details the seven sins: 1) Violation of architecture layers, 2) Creating dependency cycles, 3) High cyclomatic complexity, 4) Lack of proper unit tests, 5) Undocumented source code, 6) Duplicate source code, and 7) Coding standard breaches. For each sin, it provides examples of how SonarQube detects and reports the issue. It concludes by categorizing the different issue types SonarQube identifies in terms of bugs, potential bugs, inefficiencies, and coding styles.
Managing code quality with SonarQube - Radu VunvuleaITSpark Community
In this session we will discover different metrics that can be used when we want to measure the code quality of a project. To calculate and interpret this metrics we will use SonarQube.
This document discusses SonarQube, a tool for continuous code inspection. It begins with an overview of static code analysis and its benefits for organizations. It then covers quality gates and rules for inspection, integration with CI/CD pipelines, and IDE plugins. Static code analysis examines source code to find patterns that could impact security, reliability or maintainability. Quality gates help highlight critical areas for improvement and gauge technical debt over time. SonarQube allows for continuous inspection across multiple languages and frameworks.
Leaning on the two Ts is a talk on improving code quality, personal practise and developing good habits using tools and techniques.
Attendees of this talk will learn about my experience when learning to use static analysis tools (i.e. SonarQube) and some of its features. In addition also find out how it can be used to improve your code quality and personal practice. A number of tools will also be mentioned and how to include them in your practise.
Powerpoint-like presentation sharing information from a perspective higher than just the use of tools, although no runtime implementation or live coding will be performed. Live examples might be shown depending on time constraints.
The presentation is definitely for intermediate and experienced Java developers, but also beginner developers who would like to know more about code quality and tools that can be of help. Also for those already using static analysis or other tools in anger or just casually. Especially those who would like to know how to use tools as a medium to learn from.
SonarQube is an open source tool that can be used to automate code quality metrics through continuous inspection of codebases to identify issues, aggregate metrics across projects, and provide dashboards and reports. It supports multiple languages including C# and JavaScript, and can be integrated into continuous integration pipelines through plugins. Using SonarQube allows teams to focus QA efforts by prioritizing the most important issues, track improvements over time, and gain insights into code quality trends across an organization.
This document discusses code quality and continuous inspection. It introduces SonarQube, an open source tool for managing code quality. SonarQube can analyze code for potential bugs, complexity, duplicated code, test coverage, and other metrics. It supports many programming languages. The document emphasizes that SonarQube is a tool to help improve quality, but quality ultimately depends on human effort.
This document discusses Sonar, an open source tool for continuously measuring code quality. It analyzes code across 7 axes: coding rules, architecture, unit test coverage, duplicated code, potential bugs, complexity, and comments. Sonar provides visual reports on metrics and enables tracking their evolution over time. It supports many programming languages and integrates with tools like FindBugs and PMD. Sonar helps manage code quality in a project by identifying hotspots, issues, and allowing review of past states through its time machine feature.
SonarQube - The leading platform for Continuous Code QualityLarry Nung
SonarQube is a leading platform for continuous code quality that allows developers to write clean code, detect bugs across 20+ programming languages, and integrate with DevOps tools like Jenkins. The document discusses getting started with SonarQube by setting up the Windows service, MySQL database, security, plugins, rules, quality profiles, and quality gates. It also covers project management, remote access, using the SonarQube scanner for MSBuild, and integrating SonarQube with Jenkins for continuous code quality.
Java Source Code Analysis using SonarQubeAngelin R
This document discusses how to set up and use SonarQube to analyze code quality for both Maven and non-Maven projects. It describes installing the SonarQube server and runner, configuring the server, and setting up projects for analysis by adding a sonar-project.properties file. It then explains how to generate a SonarQube report by running the sonar-runner on a project and view results on the SonarQube server dashboard.
This document summarizes a presentation about continuous inspection and reducing technical debt in software development. It discusses the "7 deadly sins" that can accumulate technical debt, such as duplications, lack of testing, and complexity. It introduces Sonar as a tool to help developers measure and reduce these sins by augmenting their ability to reduce, reuse and recycle source code. The presentation demonstrates Sonar and outlines its future roadmap to expand its rules, metrics, and language support to further help developers improve software quality.
This document discusses technical debt governance and developer demos. It contains information about degrading velocity over time spent implementing features versus time spent on debt repayment. Charts show the effects of debt on productivity. The document also lists governance roles and mentions analyzing code with SonarQube and using pull requests to prevent debt. Demo topics are listed as the idea, experience with SonarQube, and its roadmap.
This document discusses using SonarQube to improve code quality. It introduces SonarQube and describes demonstrating its dashboard, showing code changes and quality metrics. The document outlines a strategy for using SonarQube to identify rule violations, improve test coverage, reduce complexity, and detect unused code. It lists additional SonarQube features and provides resources for learning more.
Sonar is an open source quality management platform that analyzes source code quality across 7 axes from the portfolio level to individual methods. It provides efficient navigation and tools to uncover technical debt and establish action plans. Written in Java, it can be extended with plugins to support additional languages and functionality and stores analysis results and configurations in a database for combining with historical measures.
Functional Tests Automation with Robot Frameworklaurent bristiel
The document discusses Robot Framework, an open source test automation framework written in Python. It can be used to write functional, end-to-end, component, and unit tests. Robot Framework uses a keyword-driven, data-driven, or behavior-driven approach. It provides built-in keywords and libraries that allow tests to be written in a simple, lightweight manner and executed across multiple platforms and applications. Case studies are presented on large companies using Robot Framework to automate testing of their products. Both advantages and limitations of the framework are covered.
The document discusses the concept of technical debt, which refers to shortcuts taken in software development that incur additional costs down the road. It defines technical debt as work that will cost more to do later than it would cost to do properly now. While taking on some technical debt can speed up development in the short term, it accrues interest over time as more work is needed to maintain the low-quality code. The document examines reasons for and perspectives on technical debt from both business and technical viewpoints, and discusses ways to track, measure, and strategize around managing technical debt.
A Mono- and Multi-objective Approach for Recommending Software RefactoringAli Ouni
This document outlines Ali Ouni's Ph.D. defense presentation on recommending software refactoring using mono-objective and multi-objective approaches. The presentation includes the following key points:
1. It provides context on the need for automated software refactoring recommendation systems to address challenges in manually refactoring code.
2. It describes Ouni's research methodology which involves detecting code smells, generating refactoring recommendations using mono-objective and multi-objective search-based techniques, and evaluating the approaches.
3. It covers code smell detection including generating detection rules using genetic programming from code smell examples, and evaluating the detection approach on several systems.
4. It outlines the presentation including discussing
Sonarqube is an open source platform for continuously inspecting code quality. It uses tools like Checkstyle, PMD and FindBugs to evaluate code. Sonarqube can be run locally or using Docker. The document provides instructions for installing Sonarqube on Docker and running a Maven project through the Sonarqube analysis to check code quality.
This document provides an overview of SonarQube, a code quality management platform. It discusses how SonarQube can be used to track technical debt by aggregating and trending code metrics over time. The presentation includes a live demo and discusses integrating SonarQube into development workflows as well as future plans to add more Puppet support and checks.
It is not to complicated to keep new project with good code quality for half year. Maybe, for one year. But what if team works on some project for years? Or even ”better”: you need to support and grow large project after another team. Presentation describes Continuous Inspection, main measures of code quality that will make your life better, continuous inspection and how to cook it with SonarQube.
This document discusses SonarQube and the seven deadly sins of software development it helps identify. It begins by introducing SonarQube and its role in separating developers from code quality issues. It then details the seven sins: 1) Violation of architecture layers, 2) Creating dependency cycles, 3) High cyclomatic complexity, 4) Lack of proper unit tests, 5) Undocumented source code, 6) Duplicate source code, and 7) Coding standard breaches. For each sin, it provides examples of how SonarQube detects and reports the issue. It concludes by categorizing the different issue types SonarQube identifies in terms of bugs, potential bugs, inefficiencies, and coding styles.
Managing code quality with SonarQube - Radu VunvuleaITSpark Community
In this session we will discover different metrics that can be used when we want to measure the code quality of a project. To calculate and interpret this metrics we will use SonarQube.
This document discusses SonarQube, a tool for continuous code inspection. It begins with an overview of static code analysis and its benefits for organizations. It then covers quality gates and rules for inspection, integration with CI/CD pipelines, and IDE plugins. Static code analysis examines source code to find patterns that could impact security, reliability or maintainability. Quality gates help highlight critical areas for improvement and gauge technical debt over time. SonarQube allows for continuous inspection across multiple languages and frameworks.
Leaning on the two Ts is a talk on improving code quality, personal practise and developing good habits using tools and techniques.
Attendees of this talk will learn about my experience when learning to use static analysis tools (i.e. SonarQube) and some of its features. In addition also find out how it can be used to improve your code quality and personal practice. A number of tools will also be mentioned and how to include them in your practise.
Powerpoint-like presentation sharing information from a perspective higher than just the use of tools, although no runtime implementation or live coding will be performed. Live examples might be shown depending on time constraints.
The presentation is definitely for intermediate and experienced Java developers, but also beginner developers who would like to know more about code quality and tools that can be of help. Also for those already using static analysis or other tools in anger or just casually. Especially those who would like to know how to use tools as a medium to learn from.
SonarQube is an open source tool that can be used to automate code quality metrics through continuous inspection of codebases to identify issues, aggregate metrics across projects, and provide dashboards and reports. It supports multiple languages including C# and JavaScript, and can be integrated into continuous integration pipelines through plugins. Using SonarQube allows teams to focus QA efforts by prioritizing the most important issues, track improvements over time, and gain insights into code quality trends across an organization.
This document discusses code quality and continuous inspection. It introduces SonarQube, an open source tool for managing code quality. SonarQube can analyze code for potential bugs, complexity, duplicated code, test coverage, and other metrics. It supports many programming languages. The document emphasizes that SonarQube is a tool to help improve quality, but quality ultimately depends on human effort.
This document discusses Sonar, an open source tool for continuously measuring code quality. It analyzes code across 7 axes: coding rules, architecture, unit test coverage, duplicated code, potential bugs, complexity, and comments. Sonar provides visual reports on metrics and enables tracking their evolution over time. It supports many programming languages and integrates with tools like FindBugs and PMD. Sonar helps manage code quality in a project by identifying hotspots, issues, and allowing review of past states through its time machine feature.
SonarQube - The leading platform for Continuous Code QualityLarry Nung
SonarQube is a leading platform for continuous code quality that allows developers to write clean code, detect bugs across 20+ programming languages, and integrate with DevOps tools like Jenkins. The document discusses getting started with SonarQube by setting up the Windows service, MySQL database, security, plugins, rules, quality profiles, and quality gates. It also covers project management, remote access, using the SonarQube scanner for MSBuild, and integrating SonarQube with Jenkins for continuous code quality.
Java Source Code Analysis using SonarQubeAngelin R
This document discusses how to set up and use SonarQube to analyze code quality for both Maven and non-Maven projects. It describes installing the SonarQube server and runner, configuring the server, and setting up projects for analysis by adding a sonar-project.properties file. It then explains how to generate a SonarQube report by running the sonar-runner on a project and view results on the SonarQube server dashboard.
This document summarizes a presentation about continuous inspection and reducing technical debt in software development. It discusses the "7 deadly sins" that can accumulate technical debt, such as duplications, lack of testing, and complexity. It introduces Sonar as a tool to help developers measure and reduce these sins by augmenting their ability to reduce, reuse and recycle source code. The presentation demonstrates Sonar and outlines its future roadmap to expand its rules, metrics, and language support to further help developers improve software quality.
This document discusses technical debt governance and developer demos. It contains information about degrading velocity over time spent implementing features versus time spent on debt repayment. Charts show the effects of debt on productivity. The document also lists governance roles and mentions analyzing code with SonarQube and using pull requests to prevent debt. Demo topics are listed as the idea, experience with SonarQube, and its roadmap.
This document discusses using SonarQube to improve code quality. It introduces SonarQube and describes demonstrating its dashboard, showing code changes and quality metrics. The document outlines a strategy for using SonarQube to identify rule violations, improve test coverage, reduce complexity, and detect unused code. It lists additional SonarQube features and provides resources for learning more.
Sonar is an open source quality management platform that analyzes source code quality across 7 axes from the portfolio level to individual methods. It provides efficient navigation and tools to uncover technical debt and establish action plans. Written in Java, it can be extended with plugins to support additional languages and functionality and stores analysis results and configurations in a database for combining with historical measures.
Functional Tests Automation with Robot Frameworklaurent bristiel
The document discusses Robot Framework, an open source test automation framework written in Python. It can be used to write functional, end-to-end, component, and unit tests. Robot Framework uses a keyword-driven, data-driven, or behavior-driven approach. It provides built-in keywords and libraries that allow tests to be written in a simple, lightweight manner and executed across multiple platforms and applications. Case studies are presented on large companies using Robot Framework to automate testing of their products. Both advantages and limitations of the framework are covered.
The document discusses the concept of technical debt, which refers to shortcuts taken in software development that incur additional costs down the road. It defines technical debt as work that will cost more to do later than it would cost to do properly now. While taking on some technical debt can speed up development in the short term, it accrues interest over time as more work is needed to maintain the low-quality code. The document examines reasons for and perspectives on technical debt from both business and technical viewpoints, and discusses ways to track, measure, and strategize around managing technical debt.
A Mono- and Multi-objective Approach for Recommending Software RefactoringAli Ouni
This document outlines Ali Ouni's Ph.D. defense presentation on recommending software refactoring using mono-objective and multi-objective approaches. The presentation includes the following key points:
1. It provides context on the need for automated software refactoring recommendation systems to address challenges in manually refactoring code.
2. It describes Ouni's research methodology which involves detecting code smells, generating refactoring recommendations using mono-objective and multi-objective search-based techniques, and evaluating the approaches.
3. It covers code smell detection including generating detection rules using genetic programming from code smell examples, and evaluating the detection approach on several systems.
4. It outlines the presentation including discussing
Refactoring is altering the internal structure of code without changing its external behavior or functionality. It improves code quality by increasing readability and maintainability through disciplined, behavior-preserving transformations. Successful refactoring requires codebase knowledge, a structured API, and unit testing to safeguard against unexpected side effects of changes. Refactoring takes less effort than rewriting code from scratch and helps ensure quality by keeping the system fully working after each small change.
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.
This will help you to identify the scope to refactor your code. Compiled from Refactoring: Improving the Design of Existing Code by Martin Fowler et al.
The document discusses whether to refactor code and the process for doing so. It advises being objective about whether refactoring will provide benefits or cause missed deadlines. It says to double and triple check facts before deciding and, if refactoring, to start a new Sprint 0 phase to analyze causes and revamp the roadmap rather than immediately beginning development.
Code reviews have been conducted since decades in
software projects, with the aim of improving code quality from
many different points of view. During code reviews, developers are supported by checklists, coding standards and, possibly, by various kinds of static analysis tools. This paper investigates whether warnings highlighted by static analysis tools are taken care of during code reviews and, whether there are kinds of warnings that tend to be removed more than others. Results of a study conducted by mining the Gerrit repository of six Java open source projects indicate that the density of warnings only slightly vary after each review. The overall percentage of warnings removed during reviews is slightly higher than what previous studies found for the overall project evolution history. However, when looking (quantitatively and qualitatively) at specific categories of warnings, we found that during code reviews developers focus on certain kinds of problems. For such
categories of warnings the removal percentage tend to be very high—often above 50% and sometimes 100%. Examples of those are warnings in the imports, regular expression, and type resolution categories. In conclusion, while a broad warning detection might produce way too many false positives, enforcing the removal of certain warnings prior to the patch submission could reduce the
amount of effort provided during the code review process.
ICSME 2016: Search-Based Peer Reviewers Recommendation in Modern Code ReviewAli Ouni
This document presents RevRec, a search-based approach for recommending peer reviewers for code changes. RevRec uses a genetic algorithm to combine models of reviewer expertise and collaboration. It was evaluated on three open source projects and achieved an average of 55% precision and 70% recall in recommending reviewers. The results indicate that considering both expertise and social aspects improves accuracy over expertise alone. A population-based search algorithm like genetic algorithm was found to be better suited than local search methods for this recommendation problem. Future work could integrate additional data sources to improve the expertise model and accuracy of recommendations.
Este documento discute como a ferramenta SonarQube pode ser usada para melhorar a qualidade do código, monitorando sete eixos da qualidade do código e gerenciando débitos técnicos. Explica como configurar o SonarQube, integrá-lo ao ambiente de desenvolvimento e usá-lo para identificar problemas e melhorar a qualidade do código ao longo do tempo.
Refactoring for Design Smells - ICSE 2014 TutorialTushar Sharma
This document discusses design smells in software and their impact on quality. It begins with an introduction to design quality, technical debt, and design smells. It then provides a catalog of various design smells organized by principles of abstraction, encapsulation, modularization, and hierarchy. For each smell, it provides examples from code and discusses corresponding refactoring techniques. It also discusses how smells are interrelated and how understanding their ecosystem is important for improving design quality in practice. The key takeaways are repaying technical debt through refactoring design smells to improve changeability, understandability and other quality attributes.
Refactoring is a widespread practice that helps developers
to improve the maintainability and readability of their code.
However, there is a limited number of studies empirically
investigating the actual motivations behind specific refactoring operations applied by developers. To fill this gap, we monitored Java projects hosted on GitHub to detect recently applied refactorings, and asked the developers to explain the reasons behind their decision to refactor the code.
By applying thematic analysis on the collected responses,
we compiled a catalogue of 44 distinct motivations for 12
well-known refactoring types. We found that refactoring activity is mainly driven by changes in the requirements and
much less by code smells. Extract Method is the most versatile refactoring operation serving 11 different purposes.
Finally, we found evidence that the IDE used by the developers affects the adoption of automated refactoring tools.
This document discusses code smells that indicate opportunities for refactoring, including duplicated code, long methods, long parameter lists, divergent change, and shotgun surgery. It provides examples of each smell and recommends corresponding refactoring techniques, such as extract method, extract class, introduce parameter object, replace parameter with method, move method, and move field, to improve code design, readability, and maintainability.
A Multi-Objective Refactoring Approach to Introduce Design Patterns and Fix A...Ali Ouni
This document presents a multi-objective search-based software engineering approach to recommend refactorings that introduce design patterns, fix anti-patterns, and improve software quality attributes. The approach uses NSGA-II genetic algorithm to evolve refactoring solutions. An empirical evaluation on 4 open-source Java systems shows the approach outperforms existing techniques in fixing anti-patterns and introducing design patterns while improving quality. Future work includes expanding the types of patterns addressed and developing interactive refactoring support.
Refactoring: Improve the design of existing codeValerio Maggio
Refactoring: Improve the design of existing code
Software Engineering class on main refactoring techniques and bad smells reported in the famous Fawler's book on this topic!
Recommending Refactorings based on Team Co-Maintenance PatternsSebastiano Panichella
1) The document proposes a technique called Team-Based Refactoring (TBR) that uses information about how developers work together on code (e.g. which classes they commonly edit together) to identify refactoring opportunities.
2) A case study of five Android projects evaluated whether TBR could identify useful refactorings and whether it provided complementary information to traditional refactoring techniques based on code structure, semantics, and history.
3) The results found that 74% of refactorings identified by TBR were considered useful and had medium or low perceived effort to implement. TBR also identified refactorings that traditional techniques missed in 30-43% of cases, indicating it provides complementary information.
The document discusses improving craftsmanship in coding. It recommends practices like pair programming, code reviews, and following best practices. It also stresses the importance of creating a positive work environment through communication, remote work options, and social events. Finally, it provides tips for polishing an app, listening to feedback, and gracefully handling failures. The speaker emphasizes optimizing for quality over features to achieve the final steps of awesomeness.
Releasing a great app is more than having a unique idea. It takes teamwork, collaboration and the will to be the best. At Novoda we make awesomeness happen. This talk is about our process from coding dojos to group design and iterative sprint planning with our customers. We'll describe some of our best practices as well as some of the components that can make a good app great.
This includes:
- Day-to-day processes: pairing tennis, gif code reviews and toggling hidden features.
- Work Environment: hack & tells, continuous communication & kicking ass at Tekken.
- Releasing the app: polishing & quantifying can get you top of the class and not listening to Google can get you expelled.
Finishing with some bonus Android coding tips and tricks and crazy AOSP anti-patterns.
This document introduces and summarizes Django, an open-source web framework written in Python. It highlights key features of Django, including its automated administration interface, object-relational mapper (ORM), generic views, forms, URL configuration, templates, internationalization support, and built-in user authentication. The document also discusses how Django aims to minimize the time it takes to develop software through automation, reusable apps, and other features. Examples are provided of large sites built with Django to demonstrate its performance and scalability.
Visual Testing: The Missing Piece of the Puzzle -- presentation by Gil TayarApplitools
Full webinar recording here: https://youtu.be/vx34FJYdp9o
** In this talk, Sr. Architect Gil Tayar gives a comprehensive overview of visual testing, discuss why pixel comparison is not enough, detail some of the challenges of visual testing, and review how current visual testing tools offer a solution to these challenges **
Unit tests, component tests, automation tests, acceptance tests: all the various kinds of tests check that an application works as it should, and functions as expected.
But until now, testing how an app looks like was delegated to manual testing. When all you had was a desktop or web app, that was almost acceptable: there was a lot of application to check, but only one form factor.
But in these days of multiple form factors -- desktops of various widths, mobile, tablet -- automation is a necessity. But how does one go about checking how an app looks? Comparing pixels against a baseline yields too many false positives, and any small change in your app will drown you in failed tests that you need to fix manually.
Visual Testing offers a solution to the problem. Using online services and algorithms taken from the world of AI, Visual Testing offers a platform that enables you to finally ensure that your application looks like you expect it to.
Key takeaway:
* Why functional testing is not enough
* Challenges of testing apps in a multiple-form-factor world
* How current visual testing tools overcome the challenges
* The place of Visual Testing in your tests
* How to write visual tests
Saksham Sarode - Innovation Through Introspection - EuroSTAR 2012TEST Huddle
EuroSTAR Software Testing Conference 2012 presentation on Innovation Through Introspection by Saksham Sarode. See more at: http://conference.eurostarsoftwaretesting.com/past-presentations/
Javascript unit testing with QUnit and SinonLars Thorup
This document discusses JavaScript unit testing with QUnit and Sinon. It introduces Lars Thorup and his background in software development, testing, and coaching. It then provides an overview of unit testing, explaining why it is beneficial and how to implement it. Finally, it demonstrates various QUnit and Sinon techniques for writing tests, including assertions, spies, stubs, mocks, asynchronous code, the DOM, and advanced mocking.
Testing swagger contracts without contract based testingАлексей Стягайло
Contract based testing is one of the main microservices testing solutions, but it has several restrictions: need deep knowledge about microservice, used frameworks and program language. In my talk, I will show how our team checking differences between Swagger/OpenAPI specs, generate json schemas from swagger json files and comparing json schema and real response.
The document summarizes ideas for refactoring legacy code, including safely refactoring procedural code, better testing with Groovy, and managing dependencies. It also discusses social challenges like morale and politics, and provides suggestions for starting refactoring efforts and overcoming obstacles like lack of time or traction. Refactoring techniques presented include extracting methods, dependency injection, and using static methods to reduce dependencies.
Unit Testing like a Pro - The Circle of PurityVictor Rentea
Best practices on designing unit tests, designing testable production code, a glimpse of TDD, using mocks and isolating pure functions for easy testing. Talk distilled from http://victorrentea.ro/#unit-testing
Held at VoxxedDays Bucharest in March 2019.
This document provides an introduction to web components and discusses their benefits. It explains that web components bring a native component model to HTML, allowing for reusable UI functionality both within and across applications. The document demonstrates several types of web components, including custom elements, HTML templates, HTML imports, and shadow DOM. It also discusses browser support for web components and strategies for improving support, such as using polyfills.
"Formal Verification in Java" by Shura Iline, Vladimir Ivanov @ JEEConf 2013,...Vladimir Ivanov
This document discusses formal verification and testing methods for software. It defines formal verification as mathematically proving software correctness against a specification, while testing involves running software with different inputs to check for errors. The document outlines some key differences: formal verification provides a lower bound on quality by guaranteeing absence of certain failures, while testing only provides an upper bound. It also discusses techniques like deductive verification using theorem proving. Later sections cover topics like type systems, annotations, and pluggable type checking tools like the Checker Framework.
The document discusses testing strategies for migrating from Drupal 7 to Drupal 8. It introduces various testing tools that are well-suited for Drupal 8 like PHPUnit, Behat, and PhantomJS. It emphasizes the importance of acceptance testing and behavior driven development to test business value. The document also recommends writing modular object-oriented code, using autoloaders and Composer to more easily migrate code and testing between versions.
Beyond Breakpoints: A Tour of Dynamic AnalysisC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2dXUUTG.
Nathan Taylor provides an introduction to the dynamic analysis research space, suggesting integrating these techniques into various internal tools. Filmed at qconnewyork.com.
Nathan Taylor is a software developer currently employed at Fastly, where he works on making the Web faster through high performance content delivery. Previous gigs have included hacking on low-level systems software such as Java runtimes at Twitter and, prior to that, the Xen virtual machine monitor in grad school.
Dagger is a dependency injection framework that helps manage object creation and dependencies. It allows injecting dependencies into classes rather than having them directly instantiate dependencies. This improves testability. The document discusses how Dagger is used at the New York Times, including setting up modules, components, scopes like activity scope, and testing with Dagger. Qualifiers are used to provide different implementations for different flavors. Modules provide dependencies, components compose modules and inject dependencies. This architecture allows scaling across build variants and libraries while improving testability and code organization.
The document provides an overview of a presentation about Google Cloud developer tools and an easier path to machine learning. It introduces the speaker and their background and experience. It then outlines the agenda which includes introductions to machine learning and Google Cloud, Google APIs, Cloud ML APIs, and other APIs to consider. It provides examples of using various Cloud ML APIs like Vision, Natural Language, and Speech for tasks like image labeling, text analysis, and speech recognition. The goal is to demonstrate how APIs powered by machine learning can help ease the burden of learning machine learning by allowing users to leverage pre-built models if they can call APIs.
This document provides an overview of Node.js including:
- What Node.js is and its event-driven, non-blocking architecture
- How to install Node.js and build applications
- How to use modules like Express.js and Socket.io
- Examples of deploying Node.js applications to Microsoft Azure
- A demonstration of building a collaborative drum machine app with Node.js, WebSockets, and the Web Audio API
Similar to ITAKE Unconference - Holding down your technical debt with Sonarqube (20)
This document discusses the major changes coming in JDK 9, including encapsulating unsupported APIs, removing some supported APIs, and introducing a new modular structure. The key changes are the introduction of a module system that groups code into modular units and defines dependencies, changes to the accessibility of APIs between modules, and the restructuring of the JDK itself into modules. This modularization is a major change that will impact application development but aims to make Java applications more secure, maintainable and flexible.
The document is a transcript from a presentation titled "The Art of Software Gardening" given by Patroklos Papapetrou at DevTernity 2015. The presentation proposes using the analogy of software development as gardening, where developers are called "software gardeners" and code is treated like plants in a garden. Some key aspects of this analogy include evolving design like a garden, continuous refactoring to remove dead code, treating each line of code with passion, and having skills, best practices, and tools to develop software while withstanding changes like a garden withstands weather. The goal of this analogy is to promote an attitude of care, evolution and passion in software development.
This document discusses ways to boost a development team's speed. It identifies several activities that can positively or negatively impact speed, including skills and experience, software complexity, work-life balance, and adding business value. The document recommends finding an ideal speed that mixes periods of extreme sprints with longer marathon phases. It also stresses the importance of focus, clear requirements, feature prioritization, automating tasks, and saying no to take on too much work. Regular breaks, sleep, nutrition, and limiting overtime are presented as ways to improve productivity while maintaining work-life balance.
This document provides an overview of building web applications with J2EE. It introduces key J2EE concepts and technologies including the J2EE architecture with its layered approach, containers that provide services to components, and common technologies used like EJBs, JSF, XHTML. It also discusses J2EE servers, development lifecycles and tools, and provides an example blog application to demonstrate J2EE concepts in practice. The presentation concludes with a Q&A session.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
When it is all about ERP solutions, companies typically meet their needs with common ERP solutions like SAP, Oracle, and Microsoft Dynamics. These big players have demonstrated that ERP systems can be either simple or highly comprehensive. This remains true today, but there are new factors to consider, including a promising new contender in the market that’s Odoo. This blog compares Odoo ERP with traditional ERP systems and explains why many companies now see Odoo ERP as the best choice.
What are ERP Systems?
An ERP, or Enterprise Resource Planning, system provides your company with valuable information to help you make better decisions and boost your ROI. You should choose an ERP system based on your company’s specific needs. For instance, if you run a manufacturing or retail business, you will need an ERP system that efficiently manages inventory. A consulting firm, on the other hand, would benefit from an ERP system that enhances daily operations. Similarly, eCommerce stores would select an ERP system tailored to their needs.
Because different businesses have different requirements, ERP system functionalities can vary. Among the various ERP systems available, Odoo ERP is considered one of the best in the ERp market with more than 12 million global users today.
Odoo is an open-source ERP system initially designed for small to medium-sized businesses but now suitable for a wide range of companies. Odoo offers a scalable and configurable point-of-sale management solution and allows you to create customised modules for specific industries. Odoo is gaining more popularity because it is built in a way that allows easy customisation, has a user-friendly interface, and is affordable. Here, you will cover the main differences and get to know why Odoo is gaining attention despite the many other ERP systems available in the market.
What is Master Data Management by PiLog Groupaymanquadri279
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
UI5con 2024 - Bring Your Own Design SystemPeter Muessig
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
10. What we should measure;
● Abstract numbers? (Almost) useless
● Evolution through time? Definitely!
● Metrics? Yes but which ones?
● Welcome to the seven axes of quality
13. The seven axes of quality
Design
Duplications
Documentation
Com
plexityUnitTests
Coding rules
PotentialBug
Technical Debt
14. Technical Debt
“If the debt grows large enough, eventually the company will
spend more on servicing its debt than it invests in increasing
the value of its other assets”
Steve McConnell
(Author of code complete)
15. Technical Debt – My take
“Technical debt for software systems what salt is for
cooking. You need it, you can't see it but if you don't control
it it's too late”
Patroklos Papapetrou
(Yet another software gardener)
16. This is how the source
code looks like when we
don't pay off technical debt