Krugle Basic User Guide

Getting Started

This guide explains how to use the core search and analysis features in Krugle. These features apply to all versions of Krugle (including Krugle Enterprise, Krugle Opensearch and Krugle Basic), but the instruction herein explicitly reference Krugle Basic. This is because Krugle Basic comes pre-configured with information and code projects that allow you to perform the examples in this guide. You can download and install Krugle Basic here. If you do not have a version of Krugle installed at your site, you can "test drive" Krugle Opensearch here.

To test Krugle features from the same network that hosts Krugle Basic, point your browser to the URL shown in the VMware console window. For example, in the example setup, that URL is . This will take you to the Krugle Search Environment (KSE).

Sample Projects

Let’s first take a look at the sample projects included with Krugle Basic, which can be accessed by clicking on the "Browse Projects" link to the right of the search box. The available projects include:

  • Aapche Hadoop - code files and documents crawled from the trunk of the Apache Hadoop project
  • Apache JMeter - code files and documents crawled from the trunk of the Apache JMeter project
  • Apache SOLR - code files and documents crawled from the trunk of the Apache Solr project
  • Apache SOLR Issue - issues crawled from the JIRA repository for the Apache Solr project
  • WordPress - code files and documents crawled from the trunk of the WordPress project
  • Custom CommonLibrary.NET - code files and Work Items for a sample Microsoft Team Foundation Server project


Beyond these simple examples, Krugle projects are extremely flexible and can be configured by the Krugle administrator. The administrator can also choose the set of users who may access or search projects and the frequency with which projects are re-crawled to ensure updates are added to the index. In addition, Krugle will crawl the check-in comments for projects and make these searchable too.

To search for a keyword in Krugle Basic, simply enter that string in the search box. In the example below, we have searched for "getParameter":


Search results are shown 10 per page and each result includes the name of the file, the language, the project name, when the project was last crawled, and a 5 line snippet from the file that shows the best matches from that file, and with the search term highlighted.

The search may be refined using the facets in the left-hand margin: by Project, by Document Type, by Author or by Date. In the example below, we have refined the search to return only results where "getParameter" is found in Java files within the project "Apache SOLR":


Searches may also be refined by using directives entered into the search box that allow you to take advantage of the code parsing capabilities of Krugle. For details of these, please consult the Krugle Enterprise User Manual.

Note that every set of search criteria generates a unique URL, which can be saved for later use of shared with colleagues. When a user clicking on a search URL, their browser will open an instance of the Krugle Enterprise web client and execute the specified search, returning results from projects that the given user has access to.

These URLs enable the user to take advantage of browser tabs to display multiple search results by simply opening search results in a new tab. Generally, new browser tabs can be opened on Windows via Ctrl+Click on Windows and via Command+Click on a Mac.

Clicking a file name opens the file in the browser and shows the location of the file within the project:


Krugle has built-in MIME-type detection that distinguishes between over 50 programming, scripting and markup languages as the files are crawled out of the SCM repositories.
In all cases throughout the Krugle interface, slicking on the project name from the search results or from the file display will open the Project Overview page for the project (see later section).

A powerful Krugle feature is the ability to create searches that exploit the underlying structure - and design intent - of the software code. These searches can be created with Krugle query qualifiers. Krugle query qualifiers make your searches more accurate and effective.

For example, you can use Krugle to locate keywords or "tokens" in very specific portions of the code: in comments that are included with the code files, in function or class names or in function calls, in object methods, etc. For example, to find all files that had an occurrence of the term Connection contained in a function call sequence of code, you would enter the search query functioncall:"Connection" in the Krugle search box..

A complete list of advance Krugle query qualifiers and examples can be found here.

This Krugle Basic instance includes a project that contains only entries from the JIRA bug tracking system. Krugle indexes these records and makes information such as the issue summary, description and created/modified dates searchable too. Issues are classified as a special document type named "Issue", which allows searches to be narrowed to include just Issues by clicking on the appropriate facet, as well as restricting searches by author, data and project as normal.

In the example below, the user is searching for lang:issue (search file type "Issue") associated with the user "Robert Muir" :


Clicking on the second result (SOLR-1336) displays the Issue detail page


Notice that the issue page includes a URL to the original source issue, which can also be explored:


When Krugle Basic crawls projects it also crawls and indexes the entire check-in comments for the project and makes this searchable too. This is extremely useful for finding the set of changes that related to fixing a given bug or activities for a particular user. In the example below, we have searched for a specific bug id and Krugle Enterprise has returned a single result which has the search term in the check-in comment. By clicking on the revision ID we can then explore the details of the change set.

In the example below, the user searches for a reference to a bug ("SOLR 4221") in a "Check-in Comment" for the user "yonik". The result shows the check-in comment, the Subversion revision id of the commit, and the number of files that comprised the change set:


Clicking the revision number ("1526244" in this case) in the summary for the first result takes the user to a page summarizing the commit. The page lists the files added, deleted or modified by the commit:


NEW - Krugle AutoSearch™

Krugle AutoSearch™ automatically prepares summary trend data for any Krugle query. This feature can be combined with all Krugle query and faceting options - making it possible to continuosly analyze and automatically report on a wide range of development activities.


For example, you may want to track the number of instances of a Java code pattern (e.g. method called with specific options) that results in poor performance or a memory problems - across all current projects. Or maybe you want to monitor all code submits that include a reference to "level 4" defect fixes across several, selected projects.

To begin, enter any query - as described in the preceding sections - and select the "Save" button. In the resulting dialog, enter a "Name" for your search and select a "Type" from the dropdown list - then click Save. Next, click Saved Search from the Reports link in the upper right portion of your screen. Click the detail icon (it looks like a human eye) in the Options column, from the row corresponding to the Search Name created in the previous step.


When the details dialog appears for your Search Name, click the edit icon in the upper right. Click the AutoSearch™ Yes choice. Specify a Frequency Interval (DAILY is usually sufficient), a desired start time and an email address where you want reports sent. Check the AutoSearch notification if you want to be notified whenever there is a change in results. Click Save when complete.

Once this specification is complete, Krugle will automatically run the query at the specified frequency, store/summarize query results and identify result changes since the query was last executed. Changes will be noted and results will be plotted in charts, as shown in the first screenshot of this section.

NOTE: Krugle will require several days after initial installation for AutoSearch™ results to populate. To view complete sample reports visit Krugle Opensearch.

Krugle Basic recognizes and preserves all important data relationships contained in Work Items from Microsoft Team Foundation Server. For each Work Item, Krugle automatically "deep" indexes all information in the Work Item description, all of the file attachments to the Work Item (such as MS Word, Powerpoint and Excel) and presents important relationships between work items.


To access Work Items in Krugle, enter a search term and check the Work Item Document Type. Click any of the result summaries on the right side of the result screen to view the Work Item. In this example, we've searched Krugle for "sha" and found Work Items that relate to the specifications and basic test plans for a planned implementation of a secure hashing algorithm.


View online demo here.

From this view, users can instantly access important information and relationships for the Work Item (even if aren't familiar with this particular work item or the project that it belongs to). Click the link in the Work Item field to go directly to the currently displayed work item in TFS. Click any of the attachment file link names to view the source file in its entirety. Click on any graphical elements representing Work Items in the "Related Work Items" area to view those Work Items in Krugle.

Project Pages

Clicking on a project’s name will open the Project Overview page for that project, which displays a range of information:

  • SCM Link - a drop-down list of the location(s) within the source code management system from which code and other documents are drawn (https://svn.apache.org/repos/asf//lucene/dev/trunk/solr/src/ in this case).
  • Project Directory - a browsable tree in the left margin that shows the content of the project. Selecting a folder opens that folder; selecting a file will open that file in the browser.
  • Overview Tab - a summary of project information including:
    • project’s owner
    • open source license (if any)
    • a set of links to project resources
    • a display of the number of lines of code and number of files, broken down by language, and a count of files where open source licenses have been detected. Clicking on any one of these entries runs a search for items of that type.


  • Activity Tab - charts indicating Krugle user activity related to the project, and SCM check-in activity over the last 30 days


Clicking on a bar of the chart presents additional information. In the image below, we see details for check-ins on April 1. NOTE: the date ranges and data for your version of Krugle Basic will vary from the information shown here.

Clicking on the Revision number for any of the commits on a given day will open the change set page for that commit (see Check-In Comment Search section above).


  • Reports Tab - if configured by the administrator, this contains Similarity Reports which compare the project with other projects (currently supported for Java, C and C++ code).

In this Krugle Basic instance a Similarity Report has been configured to compare the "Apache SOLR" project (which comprises code) with the "Apache SOLR Issue" project (which comprises issues):


This is a demonstration example only; although the two projects are not at all similar, the Similarity Report also compares the files within the reference project against other files within the same project.

The information contained in Similarity Reports can be applied in a number of ways, including:

  • Comparison of one branch of development with other branches, as a way to measure the "merge distance" between branches. In other words, how much effort might be required to merge the branches back together?
  • Comparison of unrelated projects, as a means of identifying cases where code has been forked or "cut and paste" from one project to another. This is a common cause of bugs since changes made in one copy may never be propagated to the others.

The figure below shows the Similarity Report which begins with two charts. The first chart shows the total number of files from "Apache SOLR" that are similar to files in the other projects, grouped by similarity category. The second chart shows a breakdown of similarity by project. Note that the first bar compares the "Apache SOLR" project against itself, which indicates that even with this project there are some near identical files, which may point to an opportunity to re-factor some code.

Below the charts is a table that shows similar file pairs across the set of projects. The table expands to show files from the reference project that are similar to files in the comparison set. The final column shows the degree of similarity as a percentage; if the similarity is less than 100%, clicking the link will open the Diff tab.

In the example above, we see that files "XsltUpdateRequestHandler.java" and "BinaryUpdateRequestHandler.java" are 71.8% similar (ignoring whitespace and comments). Clicking on the percentage will bring up a diff report that compares the two files in question.


Adding your own code

Now that you are familiar with the key functionality in Krugle Basic, you will be able to search for code, documents and related information within your organization’s projects.

Point of Note
Before adding your own code you should first delete the sample projects that came preloaded on the Krugle Basic image.

You will add (and delete) searchable code projects in Krugle Basic using the administration console. Through the administration console, you will also be able to

  • Access usage reports
  • Manage access control capabilities for Krugle Basic end users
  • Adjust the frequency of project updates

To add your own code, consult the Krugle Administration guide.

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