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How to optimize the content and the scope of the analysis

for

with Kiuwan Local Analyzer (KLA)

Table of Contents

Select the right source code to analyze

Before you start an analysis with KLA, you have to provide a source code directory. All the files available in this directory will be analyzed. The size of the source code to be analyzed affects proportionally the time and memory used for the analysis execution. 

Avoiding analyzing unneeded code is the first approach to reduce time and memory.

See our guide on Setting Source Code Filters with KLA.

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As a rule of thumb, big source files are good candidates to be excluded from the analysis, for example:

  • Auto-generated code;
  • Library components; 
  • Database exports.

To identify these large files,find the discovery.diagnosis.txt file in the temp directory of your analysis. It will show:

  • The number of files to analyze for every technology;
  • Any files bigger than a preconfigured threshold (200Kb).

Execute a ruleset according to your needs

InfoRules analysis step executes all your model’s

By default, the rules analysis steps executes all of the model's active rules for every file. 

The default model (CQM) contains aprox 900 rules,

being active aprox 700.This

of which 700 are active. This means that for every file, 700 rules will be executed on

its

their source code.

Are all active rules needed ?

In a large analysis (for example, with thousand of files), you most probably will only be interested in “important” defects.

Choose a model that suits your needs the most, activating only the rules that are important to you. 

A large set of rules will generate defects for high

importance

-priority rules as well as for

very

low-

low

priority ones.

 

Read more in our Guide to Model Management

Mute or deactivate a rule

Rules can be either muted or deactivated. 

Muting a rule means that the rule will still be executed in the background, however the results will be hidden (e.g. in the event of many false positives)

Deactivating a rule means that the rule will not be executed at all (e.g. found defects are uninteresting or do not apply to your application). Deactivating rules speeds up the analysis process and make it more manageable. 

Read more in our Guide to Model Management.

Low priority rules will generate thousands of non-important defects that will increase the resources needed for your analyses.

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Try to focus on your analysis needs. Avoid generate more defects than needed. 

Use a model that best suit your needs, activating only those rules that are really important for you. 

 To activate only important rules is the most efficient way to execute the analyses as well as to “consume” the produced results.

Mute vs deactivate a rule

Reasons for mute defects can be of different nature, being the most common to hide defects that are considered false positives.

 

But muting a defect is supposed to be something ocasional.

Bear in mind that muting a rule only “hides” its defects, but the rule is still being executed.

  1. If you are muting too many false positivesyou should immediately contact Kiuwan Technical Support (and deactivate that rule). 
  2. If the reason to mute a rule is because the discovered defects do not apply to your application or because are not of your interest, deactivate the rule.

You will speed up the analysis process and make your analyses more manageable.

Please visit https://www.kiuwan.com/docs/display/K5/Models+Manager+User+Guide on how to deactivate rules and managing Kiuwan models

 

Process JSP in Java analyses

If you are

analyzing Java

analyzing Java, there’s a configuration option that has a considerable impact on analysis performance and memory needs:

  • process JSP as Java servlets?

If this option is set to true (the default value),

 for

 for every JSP Kiuwan will internally generate its java servlet code and will execute the java rules to it.

This servlet code generation consumes a considerable amount of time and memory.

The advantage to

The advantage to generate it is

a higher

a higher precision in detecting Code Security

vulnerabilities spread and the

vulnerabilities spread between JSPs and Java files (mainly XSS). 

If this is not your concern, you can set this property to 

false 

false and the execution will be faster and will run with less memory needs.

 

Pay attention to

SQL analyses

ambiguous file extensions

 
 
Kiuwan associates source files and technologies through file extensions
.

And this association is used by KLA to execute the adequate engine on the source files.

See https://www.kiuwan.com/docs/display/K5/Kiuwan+Supported+Technologies for a full detail on extensions
and
technologies.
But 
there are some extensions that are commonly associated to more than one technology. Some examples:  
  • .

sql is a typical example, it matches PL
  • sql matches PL_SQL, Transact and Informix,

  • .c/.

h are also the case for C
  • h matches C, C++ and Objective-C

  InfoWhen running in GUI mode,

GUI mode: KLA detects such ambiguous situations and asks the user to

resolve it by selecting the adequate technology.  Then, for example, the user might select plsql because he/she knows that it’s analyzing an Oracle application.

select the correct technology.

CLI mode: KLA will execute by default every available engine, wasting time and resources producing confusing results. To solve this, search for the supported.technologies parameter

Instead, when running in CLI mode, by default KLA will execute (in the sql case) the three available sql engines, wasting time and resources and producing confusing results (as will generate defect information from all those engines and corresponding rules).

An easy way to avoid unnecessary processing is specifying supported.technologies parameter with only the proper technologies

If you know that you are analyzing PL_SQL, be sure to delete Transact and Informix from the list of supported technologies.

For further info please visit Command Line Interface - SupportedTechnologies 

Another example, it’s quite common to analyze applications that include export/import SQL scripts.

These scripts are usually huge files. If you do not exclude those script files, and do not change default sql configuration, Kiuwan will analyze those huge files with all the sql engines.

You can imagine the waste of time and resources ...
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As general rules:

  1. be careful to specify only the adequate sql engine in supported.technologies parameter.
  2. be sure to exclude export/import script files from the analysis

 

when invoking KLA in CLI mode

.

and delete the unneeded technologies. 

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titleImport/export SQL scripts

Export/import SQL scripts are quite common in applications, and those files are usually very large.

Make sure you exclude those scripts from the analysis by changing the default SQL configuration, if you want to speed up your analysis.

Duplicated code analysis

Duplicated code analysis (

aka clone

aka clone detection) is also quite

a memory and cpu intensive

an intensive memory and CPU-draining task.

Nevertheless

However, it

allows to

can be configured to modify its working mode,

then

 

As you can read in the above article, ignoring literals and identifiers its a “smart” way to find clones, but in many circumstances it’s not obvious to understand.

Most of the times, we want to identify duplicated code as “identical” code.

You can set this way of working (i.e. only detecting identical code blocks) by specifying the following properties:

Code Blocklanguagexml

reducing time and memory requirements.

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There’s a couple of aspects that affects resource consumption (mainly memory and execution time):

  1. how to manage literals and identifiers

  2. the minimum number of tokens a clone must have

This article (https://www.kiuwan.com/blog/avoid-duplicated-code-with-clone-detector/) explains how clone detector works and the different ways of configuring it.

If you are not interested at all in duplication code analysis, you can make Kiuwan not execute it:

  • In KLA CLI mode, specify ignore=clones 

Kiuwan’s clone detector searches for fragments of tokens that are very similar.

The term ‘token’ refers to each of the atomic elements identified by the analyzer. There are three types of tokens:

  1. Operators and reserved words (specific for each language)
  2. Identifiers: variable names, function names, etc.
  3. Literals: numbers and string constants used in the code.

Kiuwan also generates defects of ‘duplicated code’ according to the size of the fragments found:

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You can configure the minimum tokens that Kiuwan uses to detect a clone. This is done at two levels:

a) In Kiuwan’s Local Analyzer go to Advanced options, then configure the number of tokens to detect a clone. You can configure a different number of clones for each language. 

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b) In your model, configure the minimum tokens to generate a ‘Duplicated code’ defect.

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Depending on this configuration, Kiuwan gets different results in the clone detection. Let see these in detail.

The most conservative way

In this case, we configure Kiuwan to look for an exact match between the differents fragments:

{language}.min.tokens=20
{language}.ignore.literals=false
{language}.ignore.identifiers=false

Taking this source code as example:

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Kiuwan detects duplicate code:

Image Added


IMPORTANT: The ‘clone’ begins at the close parenthesis of line 5, but Kiuwan prints the complete line. This may be a little messy sometimes.
The detected tokens are:

Image Added 

Set the clone detector to be smarter

Now we are going to configure Kiuwan to ignore the numbers and string constants in our code:

{language}.min.tokens=20


{language}.ignore.literals=

false

true
{language}.ignore.identifiers=false

Also, the minimum number of tokens of a clon (200 by default) can be changed.

Image Added


As you can see in the picture above, now the fragment is bigger because the literals (‘3’ and ‘8’) are not taken into account.

The third option

As the third option, we can ignore literals and identifiers:

{language}.min.tokens=20
{language}.ignore.literals=true
{language}.ignore.identifiers=true

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With this last option, the most efficient one, it was clear that class DupCodeTestCopy is really a copy-paste where the class was only renamed, so Kiuwan detects the whole class as a clone.

But this configuration is also the one most prone to false positives. For example:

Image Added

Both files have a similar structure, but functionally they are very different. Ignoring literals and identifiers, Kiuwan considers both a clone:

Image Added

If clone detector raises many duplicated blocks, increase the number of tokens.

Doing so, there will be less clones, reducing this way the amount of memory needed to execute the clone detection process.

 

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Just in case you are not interested at all in duplication code analysis, you can make Kiuwan not to execute it.

  • To do it, in KLA CLI mode, specify ignore=clones at the command line.