A Library of Interview Question Algorithms

I recently spent some time revising for programming interview questions, and having done all of that work I wondered what I could do with it.

The result is a git repo of interview question algorithms that I’ve structured as a somewhat tongue-in-cheek utility; the kind of utility that i’d love to be able to use in a programming interview, but the kind would probably negate the point of the interview question in the first place.

The library solves programming questions that keep popping up, whether in books or online on sites such as HackerRank. This includes solutions to the following problems:






Sorting / Search


Project Euler

As an extra bit of practice, I also created a repo of solutions to Project Euler problems. These were mostly based on the HackerRank version of the problems, which typically require the most efficient solution to the problem rather than a single solution to a given input.

Fixing Common PowerMock Problems

PowerMock is a great tool for static / final mocking, but exhibits odd behavior in some cases. This post covers three of the most common PowerMock problems I’ve encountered.

A test class annotated for PowerMock.

Intro to PowerMock

PowerMock is useful in cases that a tool like Mockito can’t handle — namely mocking final classes and static methods.

There are many reasons why you shouldn’t aspire to mocking static methods, but sometimes it can’t be avoided.

To mock with PowerMock, you have to use the @PrepareForTest annotation, which sets up the class being mocked (it dynamically loads another class to wrap it). You can also use the @PowerMockIgnore annotation to specify classes that should be ignored when the mock occurs.

This post includes some examples of PowerMock in action, which you can find here.

Common PowerMock Problems

Mocking Core Java Classes

By default, a lot of Java system classes are ignored by PowerMock, meaning you can’t mock them the way you would any other class.

To show what happens when you do this type of mock, consider this example using the java.net.NetworkInterface class.

I have one other class in the test (creatively called OtherClass), whose call takes a NetworkInterface object, calls it’s isUp() method, and returns the result:

My first test shows that the mock is created successfully by calling isUp() method from within the test method (code):

This test passes, but if you try and use this same mock in a different class, it won’t work (code):

The NetworkInterface class, as a standard Java class, is ignored by PowerMock, so the call to isUp() doesn’t result in the expected mocked call.

To fix this, you need to @PrepareForTest the class calling NetworkInterface#isUp(), OtherClassrather than the class being mocked (code):

The test now passes, and the mock is successfully executed.

@PrepareForTest disables code coverage tools for the prepared class, which is hard to fix. The only way around this problem is to not mock Java system classes, which you could do by wrapping system class and all calls to it.

OutOfMemory PermGen Errors

PowerMock seems prone to the ‘java.lang.OutOfMemoryError: PermGen space' error, because it does so much dynamic class loading. There are some useful blog posts on this (1), but they tend to be a little too optimistic about this problem being completely fixed.

There are a number of ways to fix a PermGen error:

  1. Increase your PermGen size. Since it’s only an issue with tests, this may not be such a big deal (2)
  2. Pool mocks to limit the volume of classloading (3)
  3. Avoid using mocks entirely, instead using anonymous test classes (3)

Test Class Doesn’t Run: java.lang.VerifyError

I had this issue using PowerMock in unit test classes that had a superclass.

It happens because Oracle made Java’s bytecode checking stricter in Java 7, so it can be fixed with by stopping this checking using -noverify as a JVM argument.

If you’re not the kind of person to take that as an acceptable answer (good call), I fixed this issue by updating my version of the JDK (in my case from 7_72 to 7_79). It’s not clear that one version of the JDK definitively fixed this problem (people seem to report it as being fixed before 7_72, for example), but it’s worth updating the JDK and your version of PowerMock before resorting to -noverify.

Other Resources

Creating a WordBrain Solver

I became quietly obsessed with an iOS game called WordBrain over the holidays, and thought it’d be interesting to write some code to solve it’s puzzles (you can find the result here).

A basic single-word game.

The game itself is similar to a typical word search puzzle where, given a grid of letters, you have to find the words in the grid.

It differs in that words don’t have to be found in straight lines, so individual letters can be joined across diagonals, and as each word is found, it is removed from the grid and the remaining letters fall down. This means that for harder puzzles it isn’t possible to connect the letters in the last word unless you have found the preceding word(s).

Before starting, you know how long each word is and the order in which words must be found (as the 5 word puzzle below illustrates).

A more elaborate multiple-word game.


My solution started out as a simple word finder — finding all words of a certain length — but evolved into a more complete puzzle solver.

First, given a grid and a set of word lengths, the solver comprehensively scans the the grid for words matching against the first word’s length. It then recursively scans for the subsequent words, based on the updated grid (which is created by removing the preceding word(s)). This creates a set of potential solutions to the puzzle.

To speed up search, I created a trie dictionary to allow the solver to terminate when a non-english prefix is found.

Then, to narrow down the set of possible solutions, I used a corpus of English word frequencies to rank them by the most commonly used set of words. This tends to match the expected solution, because WordBrain uses fairly common words.

Finally, I created a Swing UI to make it easier to enter problems and view solutions.


The trie solution is significantly faster than the alternative, an in-memory set of words that didn’t allow early termination based on prefixes.

The solver itself could be slightly more space efficient, because it creates new a string through each recursive call rather than modifying a character array. However, as the search continues onto successive words, some of this copying is necessary (the updated grid, the words found in each solution, etc.).

The Swing UI isn’t particularly well written, but it does display everything required for a solution. First, potential solutions are shown next to the grid:

Potential Solutions
Potential Solutions

Next, for a particular solution, the highlighted word is shown in a decreasing shade to show the order in which letters must be selected:

Solution Illustration
Solution Illustration

Overall, this was a relatively interesting side-project, with a few things such as the trie and solution sorting which added a little depth to the implementation.

That said, I’d recommend completely ignoring this code and playing WordBrain unassisted. It’s a lot of fun!

Spie Charts Explained (+ Chart.JS Add-On)

I recently discovered spie charts, and thought it’d be interesting to try to create one in javascript… so here we are! This post introduces spie charts and discusses a spie chart extension I created for chart.js.

You can find the working code for the chart.js extension on GitHub.

What is a Spie Chart?

A spie chart is an overloaded version of a pie chart, where you can vary the height of each segment along with its width. This enhancement enables various things, such as the comparison of a dataset in two different states, or the addition of extra dimensions to a single dataset.

The next two sections use examples from the original paper on spie charts to illustrate these uses (though my data is totally made up).

Type A: Comparison of States

To compare a dataset in two states, the original dataset can be segmented in a regular pie chart, with the difference between this and the next state illustrated by the varying height of each segment.

Imagine government expenditure for two distinct years charted in separate pie charts. The change between the two charts is so marginal that it is difficult to determine what changed.

With a spie chart, expenditure in one year can be represented by the width (angle) of each segment in the chart, and the change in spending for the next year can represented by the height (radius) of each segment. This allows you to visualize both the relative weight of spending in each sector, and how that spending has changed over time:

In this example, some segments are broken down into multiple slices, where the outer slice illustrates how much spending has been reduced between the two reports.

Type B: Additional Dimensions

Alternatively, the height can be used to show a size relative to one value, while the width shows the same size relative to another.

In the following example (again based on one in the Feitelson paper), the spie chart conveys road casualties by age and sex. The height of each segment is used to indicate the number of casualties relative to the population size of that segment, while the width is used to indicate the proportion of casualties between age ranges (segments).

This example also shows how slices can be used to convey even more information, such as a breakdown of casualties in a segment between pedestrians, car riders, and (motor)cyclists.

The key characteristics of the spie chart in these examples are the ability to vary both the height and width of each segment, along with the ability to slice each segment into distinct sections.

Creating a Spie Chart

I am no expert with JavaScript, so creating the spie chart was an interesting process. If you want to skip that and jump straight to code, you can find it on GitHub, with examples here, and code here.

I decided to use chart.js as the basis for the chart, which proved to be a good decision, as it’s nicely written and extremely extendible.

Chart.JS Structure

Chart.js consists of a core file — named Chart.Core.js — and several others, with one for each type of chart supported. I decided to copy and extend the polar area chart, since it allows for pie-like segments that have different heights (though each segment has the same width).

Each of the chart type files has the same general structure, but i’ll discuss the Chart.PolarArea.js file here.

At the top of the file, a defaultConfig object provides defaults to be used for the chart’s display, including whether a scale is shown, whether animations are used, and the specifics of these features.

The majority of the code is contained within an extension of the Chart.Type object, which is the base class for all of the chart types. For a polar area chart, this contains an array of segments (the SegmentArc type), and set-up code for displaying a scale and tooltips.

The key functions in the PolarArea chart type are:

  • addData(), through which incoming data is passed from the calling function, and parsed into individual SegmentArc objects.
  • draw(), which is (somewhat obviously) called to draw the chart. When an animation is used for the chart, this function is called repeatedly, with the size of each segment gradually increasing as a result of a call to an easing function.

Each call to draw() adjusts the size of each segment by changing the start and end angle of the segment, and then the draw() function for each segment is called. This draw function is implemented in the Chart.Arc type in the core file and is common to pie charts and polar area charts.

Writing the Spie Chart

The basis of my spie chart is the existing polar area chart, modified to support variable width segments and slices. The Chart.Core.js file remains un-altered.

To begin, I updated the expected input format of the chart to allow for each of these changes.

The input format looked like this for a given segment:

But now looks like this:

I created a new Chart.Arc type, called SliceArc, and updated the addData() function to parse the incoming slices into this object (meaning the SegmentArc object contains many SliceArc objects).

The draw() function is significantly updated, with the main change being that each slice needs to be updated as the chart is drawn, rather than each segment (essentially, more looping).

The SegmentArc.draw() function is overridden, and now draws individual slices in a segment rather than the whole segment in one call. (I think this logic should be moved to the SliceArc call itself, which may make it possible to call the original Arc.draw() function without modifying it)

Tooltips were particuarly challenging to fix, because the existing inRange() and tooltipPosition() functions are very much designed around the location and position of the segment, and not slices. To avoid modifying the showTooltip() function (which is relatively large and in Chart.Core.js [called here]), I pass a slice object and make it look like a segment object (this is more than a little hacky).

Both share the same base class, so this in some ways reflects the extensible design of chart.js, but it involved adding some state to the slices that I don’t think would have otherwise been duplicated from the segment object.

Finally, I updated the tooltips template, which now iterates through slices as well as segments, and highlights slices on hover rather than segments (an implementation of which is shown in the spie-enhanced.html example).

Thoughts on Solution

Now that I have a better understanding of the structure of the codebase, I think I could do a better job modularizing certain functions and re-using existing core components. That said, i’m impressed how easy it was to create the new chart, and how little new code had to be written.

I thought about trying to allow the chart to behave like a multi-level pie chart, but there are already solutions for this, and adding an extra dimension would make things even messier.

Creating a Java REST API With Apache Jersey

In our latest development cycle we’ve been working on creating an official API to manage and control AetherStore. As part of this process I’ve been experimenting with Jersey 2, the Java reference implementation for JAX-RS, the Java API for RESTful Web Services.

This post discusses an example API using Jersey. The example is itself fairly well commented, explaining why certain pieces of code are needed, and how they relate to the project. This post covers the structure of the project and discusses some of its more interesting features.

The example is of an API representing a set, which allows:

  • Strings to be added as part of a GET request parameter
  • Strings to be added as part of a PUT request body.
  • The set of all strings stored to be returned.

If you’re using Jersey, it’s important to note that a lot of examples online use Jersey v1, which causes problems because this version of Jersey uses an entirely different namespace — v1 usescom.sun.jersey, whereas v2 uses org.glassfish.jersey, and a number of classes are either named differently, or are in different sub-packages.

The code on GitHub should work straight out of the box if you’re using Eclipse for Java EE.

Areas Covered

The code is useful if you’re interested in one of the following features in relation to jersey:

  • Running in eclipse.
  • Setting up appropriate Jersey Maven dependencies.
  • Setting up your web.xml to work with Jersey.
  • Creating a basic API.
  • Using JSON to wrap requests and responses.
  • Using an exception mapper for more readable error handling.
  • Injecting dependencies into resource classes (the API classes).
  • Unit testing Jersey.
  • Mocking calls used by our Jersey API.
  • Unmarshalling responses from a Jersey API.

Reading the Code

This section describes how the code is structured at a high level. There are some comments on specific lines of code, but I’d recommend looking at the comments in the code for a closer look at individual features.

The Apache Jersey Core

At the core of a Jersey application is the pom.xml file, which specifies all of the dependencies in the code — including Jersey itself — and the versions being used. If you’re using another example, it’s important to note the version of Jersey you’re using. Here we are using Jersey 2.6:

The web.xml file specifies how your servlet is named, and where the main Jersey Application class is. This Application class is used to start your Jersey servlet.

In this example our application class is called SetApplication. It registers bindings for a few classes, which we’ll discuss later. At this point the most relevant call to our application is:

This tells the servlet container where to look for the resource classes that form the API. In this case our resource class is called SetResource.

SetResource (API) Class

To recap, this project implements an API which supports two calls to add a string to a set (/set/add/<value> and /set/add), and a single call to get all entries in the set (/set/get). The class definition for this class annotated (shown below), to set the base path of the API call to be /set:

Then the methods in this class are further annotated to describe the API calls under this path. For example, the following code is executed when a /set/add/{value} request is made.

The full call to this method will be /set/add/{value}, where value is a variable that is mapped to the value parameter of the method as a result of the @PathParam annotation. We want the call to consume and produce a JSON response, so we specify this in the @Produces annotation. The marshalling to JSON is handled automatically, but we have to specify the marshaller dependency in thepom.xml file, as follows:

There are two methods which include the /add path, but they accept different numbers of inputs so there is no conflict. The class also contains an @Inject annotation, which tells the container to inject a dependency into the callHandler field. If we look back at the SetApplication class, we can see that this value is injected by registering it with the application through the register() call:

Unit Tests

I’ve written two unit test classes. One, SetApiTests provides what are essentially end-to-end integration tests, which call the API and check that its operations perform as expected. The second,MockedSetApiTests provides an example of using mocking to test just the API calls themselves. Both test classes extend JerseyTest, which handles the heavy lifting of setting up a servlet container and providing the API. To run correctly, JerseyTest requires a test framework provider, which is the servlet container used to run the test. In this example I’ve used the jetty container, where the dependency is specified in the pom.xml class with the following code:

In the MockedSetApiTests class, the SetCallHandler, which manages the logic behind the API call (and is injected), is mocked out:

The configure call is required by JerseyTest to properly configure the application, which in this case requires us to pass the mocked dependency so that it can be injected into the SetResource. In the tests themselves, the call to the API is relatively simple:

This makes the API call and returns the result (whether it is a success or failure to theresponseWrapper). This can then be queried to establish whether the call was successful (by getting the HTTP response code):

If successful, we can then obtain the returned value:

The test addMultipleSingleCall shows an example of an API request with a message body (in this case a set of Strings), also showing how to package up this request parameter in the test:

The examples in this post don’t include any parameters that are non-standard Java types, but doing so is relatively simple. By default only the public fields in the class are serialized, and a default constructor is required, but the class doesn’t have to implement serializable.

How To Run

To run this example in Eclipse for Java EE:

  1. Download / clone the code from GitHub.
  2. In Eclispe, go to File -> New -> Java Project
  3. Untick ‘Use default location‘ and navigate to the path of the jersey2-example repository.
  4. Re-tick the ‘Use default location‘ option, which sets up the project name as jersey2-example.
  5. Click finish to create the project.
  6. To run, either run the unit tests in JUnit, or right-click on the project and select Run as -> Run on Server.

If you want to create your own eclipse project, you can follow this example, but note that this is for Jersey v1, so you need to adjust the options used in steps 3 and 5 (i’d compare them to the example in my GitHub repo).

To run standalone (the previous steps are required):

  1. Right-click on the project, Export -> WAR File.
  2. Set the location to store the WAR file, and change the specified server runtime if necessary.
  3. Run the WAR in your favorite application server, or standalone with Jetty Runner.

Additional Resources

The following links are resources I found useful in writing this example. Where the examples refer to v1 of Jersey I’ve said so — examples are included because some part of them is useful, but be careful to note places where v1 specific code is used. This includes anywhere where a com.sun.jersey namespace is used.

This is a repost of a blog I wrote over on the AetherWorks Blog earlier this year.

CrossRef++ (A Microsoft Word Add-in)

This is a Word 2010 that replaces Word’s in-built ‘add cross-reference’ tool. Why? — because it has annoyed me greatly whilst writing my thesis!

Installer (EXE Version).

Source code as ZIP, on Github.

Please note that this only works in Word 2010, not on any earlier versions.

Why this is ‘needed’

The standard word tool (pictured below) quickly becomes tedious to use in large documents for a number of reasons:

  • You have to switch between references for things (like figures and numbered items) constantly, and you have to use a drop-down box to do this.
  • It doesn’t remember your last referenced item, so if you’re constantly referencing a figure that is two pages down the list, you constantly have to scroll down to that reference.
  • It doesn’t remember the size of your reference box, so even if you have a massive monitor, you can only ever use a tiny fraction of it to search for references.

What CrossRef++ Does Differently

  • References are displayed in a task pane, which typically stretches the length of a screen, but can be moved around as well.
  • It remembers (roughly) where your last used reference was for each type (figure, numbered item, etc.), so you don’t have to scroll as much as before.
  • It provides a few big buttons to at the top to change reference type, which makes it quicker to change.

What it Doesn’t Yet Do

  • It doesn’t support all types of references (for endnotes and other things you need to use the old tool).
  • It doesn’t handle re-sizing the task pane well.
  • It doesn’t allow you to search through references, though I’d like to do that eventually.