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Technical Debt Report: November, 2012

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Technical Debt Report: November, 2012

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TechDebt.org  is the first collaborative & open benchmarking dashboard on Technical  Debt and Software Quality. The site provides you with several metrics regarding the  technical debt for a large panel of applications. Information is anonymously collected  thanks to Scertify Refactoring Assessment , an open-source plugin for Sonar. Whenever an  audit is performed on Sonar, the plugin anonymously collects some metrics and passes them to the site.

This is the fourth report based on TechDebt.org. If you have checked the site lately, you must have noticed that it has been totally remade. Not only does it use a much more beautiful interface but it also provides many additional metrics. We are going to take you through the various panels, metrics and charts proposed. Hopefully, this will help you understand and analyze technical debt, with more figures, more details, more knowledge.

Database evolution

The first thing to notice is the apparition of a chart which allows you to see the evolution of  the database since the beginning.  As you can see,  the number of lines of code keeps growing. There is now almost 32,900 projects tracked, for a total of 410 MLOC (as of November 27th).

Illustration #1 Database evolution over time

Many thanks to all contributors and welcome to a new one : The European IT and Certification Institute.  A section of  the site is now dedicated to all  contributors. If you would like to see your name, your logo and a direct link to your site,  please contact us. This will  prove your determination to fight  technical debt!

With this new version of TechDebt, you can now see the repartition of lines of code (LOC) by language. It used to be available in the monthly reports,  like this one,  but  now you are able to see it  directly.  As you can see on illustration 2, Java is still the most present in the database, with 63% of the overall  lines of code.  C#, C++ and Javascript  are also quite present with respectively 7, 11 and 11% of LOC.

Illustration #2 LOC by language

Metrics' history

Another interesting thing with this new version of TechDebt.org is that for all  metrics, you can see their evolution since last month. This way, you can see if the overall quality of the applications tend to improve or  decrease.

For example,  the average number of  blocker violations (chart 4) per audit has been increasing so far : it is now of 147.74. A blocker violation can be a security flaw, a bug, a performance issue... Such violations should be dealt with as soon as they appear in code.

Illustration #3 Average code duplication in an audit

Illustration #4 Average number of blocker  violations in an audit

Code coverage by unit tests and code duplication (chart 3) have also been going  in  the  wrong  direction.  Code  duplication  especially  has  seen  an important growth.  This is dangerous since it makes the code expensive to maintain and to evolve : any modification must be duplicated  and each bug must be fixed in several places.

On the other hand,  Rules compliance and  Complexity (chart  5) are quite stable. Complexity increased in the beginning of the database, but it is now stable and we can even see a small improvement. Of course, this is a really small  improvement, but that's better than nothing. We will  keep an eye on this in the following weeks and hope to see a real decrease.

Illustration #5 Average rules compliance and code complexity of applications

Detailed analysis technical debt 

In the panel Technical Debt, you have access to the “Technical Debt density by Technology”. As a reminder, we define technical debt as the time needed to correct defects. This can be easily converted to dollars, as soon as you know what  is the cost of  an hour  of  work.  The  Technical  debt  density is defined as the average technical  debt  in 1000 lines of  code,  i.e.  the time needed to correct those 1KLOC.

One could be tempted to compare languages between each other, to see which one has the highest technical debt density.  However, this is a tricky move. Indeed, defects in code are found thanks to code analysis tools that verify set of rules. The fact is, so far in the database there are more Java rules (908)  than C++ rules (132,  to see which are the rules for  various languages, you can go to the Rules panel). As a result, more defects are detected in Java than in C++. One should rather compare programs written with same languages.

Illustration #6 Techincal debt density by language

According to chart 6, we can see there is nearly 6 hours of technical debt in 1KLOC of Java code. This is huge! On the same panel, you can get more  detailed information,  like the technical  debt  detected for each rule,  or the technical  debt  distribution according to rules' criticity (chart 7). As we can see on this chart,  in Java 60% of  technical  debt  is due to errors with a criticity from minor to major. That's probably why technical debt is allowed to spread across applications :  individually,  a minor (even major) violation is not so dangerous, so one is tempted not to correct it. But when you look at  the big picture, those mistakes accumulate and cost a lot.

Illustration #7 Technical debt by language and criticity

Hopefully,  some of  this technical  debt  can be suppressed at  a low cost, which brings us to the next panel : Debt write-off.

Debt write-off thanks to automatic refactoring

Debt  write-off  is  the process  of  suppressing technical  debt  at  low cost,  thanks to automatic refactoring. Indeed, some of the technical debt is due to coding mistakes that  can be automatically corrected using Scertify,  the code analysis and refactoring solution developed by Tocea.  This panel  is dedicated to the analysis of the debt write-off potential.

So far, Scertify is  able  to refactor Java applications, so this panel is dedicated to Java language.  Illustration 8 is a screen-shot  of  the write-off  summary.  As  you  can  see,  thanks  to  automatic  refactoring,  45.2% of  applications' technical debt can be suppressed. This represents an economy of 775 years or $67.41M. Quite a lot of time saved, isn't it?

Illustration #8 Debt-write off possibility

There is another interesting chart where you can see the possible write-off for each rule's criticity (chart 9). As you can see, blocker violations are hard to automatically correct. Indeed, they are often due to particular cases and very specific situations. Of  course,  minor and info violations are often the easier to correct and their correction can be automated most of the time. It is interesting to note that  critical  violations can be automatically refactored quite efficiently. This is a huge deal since those violations heavily impact an application's robustness, performance or maintainability.

Illustration #9 Refactoring opportunities by criticity

Finally, on the same panel you get access to all refactorable rules, which are sorted by criticity and number of violations. You can browse the list to get a detailed  view of  refactoring  opportunities.  The  set  of  rules  that  can  be refactored keeps increasing as Tocea adds rules to Scertify. So keep an eye on this chart  and watch for  new refactoring possibilities. Also,  if  you have ideas of new rules that should be implemented, please contact us. We are opened to suggestions!

Detailed analysis of violations

There is one panel  we haven't  talked about  yet  :  the Rules panel.  It  is dedicated to the analysis of violations of all languages, with a high level of  detail. According to the language you are interested in, you can browse a list of violations sorted by their number of occurrences. You can also see the repartition of violations according to their criticity.

As we can see on the Java chart (illustration 10), rules with lowest criticity have the most violations. On the other hand, blocker and critical  violations represent only 2 and 6% of violations. This is what could be expected, since highly critical  errors  should be less present  and treated with more  care.  However,  2%  represent  1.2M  of  violations,  which  should  not  be disregarded: there is some space for improvement!

Illustration #10 Java violations by criticity

If we take a look at the C# chart (illustration 11), we can see that almost all  violations have a criticity of major. It seems like C# rules repositories are not  finely  configured.  So  here's  a  quick  message  to  Scertify  Refactoring Assessment users that audit C# applications : you should take some time to configure rules'  criticity. This may seem like a pain, but it's worth spending some time. This will allow you to quickly identify important violations and to set up efficient correction plans. Soon enough, you'll be glad to have done this configuration.

Illustration #11 C# violations by criticity


This fourth report is coming to an end. It has presented the new features of TechDebt.org.

The site now lets you track database's growth over time. You also have access to more detailed information about the languages present in database. Thanks to metrics' history, you can track their evolutions and see how technical debt evolves. Through new figures and charts, you can get into the details of technical debt and you can see what can be automatically refactored thanks to the Debt write-off panel. Finally, you can browse detailed lists of violations to precisely identify technical debt's sources.

We hope you will find precious information in this new version.

As  always,  if  you have  some  remarks  or  if  you would like to  see specific  things  in  the  next  month's  review,  please  let  us  know:  contact@tocea.com

Opinions expressed by DZone contributors are their own.


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