SonarQube Up and Running

SonarQube 3.6.3 + Maven 2.0.11 + PostgreSQL

PostgreSQL Setup

STEP 1: Make sure postgres binaries  are available in your PATH.


STEP 2: Initialize postgres db, execute the following command

initdb -D datapath


STEP 3: Start the database, execute the following command

pg_ctl start -l “E:\logs\postgres\sonar.log” -D “E:\data\postgres\sonar”


STEP 4: Verify that postgres is listening on the default port (5432)

netstat -aon | findstr


STEP 5: Set up the sonar database

Create user postgres, and database sonar, by  executing the following command

createuser -s -r postgres

createdb sonar


connect to sonar databaseas, by  executing the following command

psql sonar


Create user sonar and grant all the permissions, as sonar will create tables/indexes/constraints for you, by  executing the following command

create user sonar with password ‘sonar’;

grant all on database sonar to sonar;


Download SonarQube

Download SonarQube 3.6.3, Version 3.6.3 is compatible with Maven 2.0.11


Configure SonarQube

STEP 1: disable the default H2 database by commenting the line number 48 of SONAR_HOME/conf/


STEP 2: Enable postgres database by un commenting the line number 86 of SONAR_HOME/conf/


Start SonarQube

Goto SONAR_HOME/bin, and based on the OS, go to the corresponding subdirectory, in my case it is windows-x86-64, so the full path would be SONAR_HOME/bin/ windows-x86-64

Double click on StartSonar.bat


First time you would see lots of output, as it will create tables/indexes/constraints etc.

and finally you will see something like this, as you can see that the application has started on port 9000


If you list out all tables in the current database, by executing \dt command in psql, you will get something like this


Point your browser to http://localhost:9000/


SonarQube is up and running now.

Why SonarQube

Since software development has started, there has been a chaos and people always ask “Is it done right?“, and we get a mix of answers

  • The Oldest one: Well it compiles
  • It seems to work
  • The universal favorite: The users aren’t complaining (Until the user starts complaining or we have to add new feature, then we can figure out how well we did it)
  • Most recent answer is the automated test cases (How do you know if you have enough tests, further what about the things which can’t be covered by tests)

How do we evaluate the quality of the code and the developer who has written the code? It is easy enough to evaluate factory worker ( what is produced with acceptable quality),  lawyer (cases won) etc.

Well it can be answered if we can measure software quality. Software quality can be defined easily through abstractions, examining it from different perspectives and rating it along different dimensions

Let’s put ourselves into a test, let’s see if we can read this.

I cdnuolt blveiee taht I cluod aculaclty uesdnatnrd waht I was rdgnieg. The phaonmneal pweor of the hmuan mnid. It deosn’t mttaer in waht oredr the leteerrs in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The rset can be a taotl msess and you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe.

The preceding text does not contain one single word spelled correctly but proves to be readable. From a product perspective, someone could support that although the text is flawed it does the job, since it manages to remain understandable. But this has the side effect of deteriorating the final reading experience, requiring additional effort to reconstruct the words and phrases. The reader unconsciously stresses his mind in an effort to adapt and decipher the messed-up words. On the other hand, the editor assigned to improve or add to the text would have to cope with this non-standard writing practice delaying the whole process.

Switch the corrupt text for a software product’s source code. The reader is now the end user of the product and the editor the developer. They both experience product quality differently, each one from their own views. The end user from a functional perspective while the developer from a structural one.

Software quality measurement is a quantitative process summing up weighted attribute values, which in part describe specific software characteristics. For each characteristic, a set of such measurable attributes is defined.

Now the question is, what are software characteristics?  Well it could be:

  1. whether  the coding has been done following a specific convention.
  2. whether well known/established good practices has been followed and well known/established bad practices has been avoided
  3. Is there any potential bugs and performance issues, security vulnerabilities
  4. Is there any duplicate code
  5. Is the code logic very complex.
  6. whether the public API has good Documentation and comment
  7. whether the code has unit tests.
  8. whether the code follows good design and architecture.

How do we define the corresponding attributes?

Attribute Weighted Value
Blocker 5
Critical 4
Major 3
Minor 2
Info 1

After having the software characteristics defined, the next question which comes to our mind is how do we enforce it automatically? The answer lies in static code analysis

Static Code Analysis


Static code analysis is a collection of algorithms and techniques used to analyze source code in order to automatically find potential errors or poor coding practices. The idea is similar in spirit to compiler warnings (which can be useful for finding coding errors) but to take that idea a step further and find bugs that are traditionally found using run-time debugging techniques such as testing.

Static code analysis, also commonly called “white-box” testing, looks at applications in non-runtime environment. It is the only proven method to cover the entire code base and identify all the vulnerable patterns. Static code analysis is also considered as a way to automate code review process.

The tasks solved by static code analysis software can be divided into 3 categories:

  • Detecting errors in programs.
  • Recommendations on code formatting. Some static analyzers allow you to check if the source code corresponds to the code formatting standard accepted in your company.
  • Metrics computation. Software metrics are a measure that lets you get a numerical value of some property of software or its specifications.

There are many static analysis tools available. However, Checkstyle, PMD, and FindBugs are well known and used in most of the projects



Checkstyle is an open source tool that can help enforce coding standards and best practices, with a particular focus on coding conventions. Although Checkstyle does cover some static code analysis features (In much the same way as PMD and Findbug), however we will mainly concentrate on Detecting and Enforcing Coding Conventions with Checkstyle.

Main Focus : Conventions



PMD is a static code analysis tool, capable of automatically detecting a wide range of potential defects and unsafe or non optimized code (bad practices). Whereas other tools, such as Checkstyle, can verify that coding conventions and standards are respected, PMD focuses more on preemptive defect detection (ensure good practices are followed). It comes with a rich and highly configurable set of rules, and you can easily configure which particular rules should be used for a given project.

The bad practices type consists of well known behaviors that almost systematically lead to difficulties over time. Here are a few examples of bad practices:

  • Catching an exception without doing anything
  • Having dead code
  • Too many complex methods
  • Direct use of implementations instead of interfaces
  • Implementing the hashcode() method without the not equals(Object object) method
  • Synchronization on Boolean (could lead to deadlock)
  • May expose internal representation by returning reference to mutable object

Main Focus : Bad practices




Findbug is another static analysis tool for Java, similar in some ways to Checkstyle and PMD, but with a quite different focus. Findbug is not concerned by formatting or coding standards and only marginally interested in best practices: in fact, it concentrates on detecting potential bugs and performance issues. It does a very good job of finding these, and can detect many types of common, hard-to-find bugs. Indeed, Findbug is capable of detecting quite a different set of issues than PMD or Checkstyle with a relatively high degree of precision. As such, it can be a useful addition to your static analysis toolbox.

Main Focus : Potential Bugs


HP Fortify


As per the HP Fortify website,

HP Fortify Static Code Analyzer helps verify that your software is trustworthy, reduce costs, increase productivity and implement secure coding best practices ……

Key features

  • Reduce business risk by identifying vulnerabilities that pose the biggest threat
  • Identify and remove exploitable vulnerabilities quickly with a repeatable process
  • Reduce development cost by identifying vulnerabilities early in the SDLC
  • Educate developers in secure coding practices while they work
  • Bring development and security teams together to find and fix security issues

Main Focus : Security Vulnerabilities



Sonar collects and analyzes source code, measuring quality and providing reports for your projects. It combines static and dynamic analysis tools and enables quality to be measured continuously over time.  Everything that affects our code base, from minor styling details to critical design errors, is inspected and evaluated by Sonar thereby enabling developers to access and track code analysis data ranging from styling errors, potential bugs, and code defects to design inefficiencies, code duplication, lack of test coverage, and excess complexity. The Sonar platform analyzes source code from different aspects and hence it drills down to your code layer by layer, moving from module level down to class level. At each level Sonar produces metric values and statistics, revealing problematic areas in the source that require inspection or improvement.

Main Focus : Each dimension from different perspective utilizing Checkstyle, PMD, Findbug, Fortify and many other static/dynamic code analysis tools


Why SonarQube

You may wonder if sonarQube uses existing proven tools then why use it at all? You can just configure these tools as a plugin in the CI server and bang we will be done. Well not necessarily, well there are lots of caveats.

  • As of now CI tools does not have a plugin which would make all these play togeather
  • As of now CI tools does not have pugins to provide nice drill down features as sonarqube has.
  • CI plugins does not talk about over all compliance value.
  • CI plugins does not provide managerial perspective.
  • As of now there is no CI plugin for Design/Architecture issues
  • It does not provide a dashboard for overall projects quality.

Features :

  • SonarQube doesn t just show you what s wrong. It also offers quality-management tools to actively help you put it right
  • SonarQube s commercial competitors seem to focus their definition of quality mainly on bugs and complexity, whereas SonarQube s offerings span what its creators call the Seven Axes of Quality
  • SonarQube addresses not just bugs but also coding rules, test coverage, duplications, API documentation, complexity, and architecture  and provide all these details in a dashboard.
  • It gives you a moment-intime snapshot of your code quality today, as well as trending of lagging (what s already gone wrong) and leading (what s likely to go wrong in the future) quality indicators
  • It provides you with metrics to help you take right decision (In nearly every industry, serious leaders track metrics. Whether it s manufacturing defects and waste, sales and revenue, or baseball s hits and RBIs, there are metrics that tell you how you’re doing: if you’re doing well overall, and whether you’re getting better or worse.)

What makes Sonar really stand out is that it not only provides metrics and statistics about your code but translates these nondescript values to real business values such as risk and technical debt. Sonar not only addresses to core developers and programmers but to project managers and even higher managerial levels as well, due to the management aspect it offers. This concept is strengthened more by Sonar’s enhanced reporting capabilities and multiple views addressing source code from different perspectives.

From a managerial perspective, transparent and continuous access on historical data enables the manager to ask the right questions.

Note: Sonar is no way competing with any of the above Static analysis tools rather it complements and works very (really very) close with these tools. In fact it seizes to work if these static analysis tools (Checkstyle, PMD and Findbug) does not exists. Further we can integrate Fortify with Sonar using this plugin