Testing and Coding…Concurrently
Data Visualization
Visualizing Defect Percentages with Parallel Sets
Sep 24th
Prof. Robert Kosara’s visualization tool, Parallel Sets (Parsets) fascinates me. If you download it and play with the sample datasets, you will likely be fascinated as well. It shows aggregations of categorical data in an interactive way.
I am so enamored with this tool, in particular, because it hits the sweet spot between beauty and utility. I’m a real fan of abstract and performance art. I love crazy paintings, sculptures and whatnot that force you to question their very existence. This is art that walks the line between brilliant and senseless.
When I look at the visualizations by Parsets, I’m inclined to print them off and stick them on my cube wall just because they’re “purty.” However, they are also quite utilitarian as every visualization should be. I’m going to show you how by using an example set of defects. Linda Wilkinson’s post last week was the inspiration for this. You can get some of the metrics she talks about in her post with this tool.
For my example, I created a dataset for a fictitious system under test (SUT). The SUT has defects broken down by operating system (Mac or Windows), who reported them (client or QA) and which part of the system they affect (UI, JRE, Database, Http, Xerces, SOAP).
Keeping in mind that I faked this data, here is the format:
DefectID,Reported By,OS,Application Component
Defect1,QA,MacOSX,SOAP
Defect2,Client,Windows,UI
Defect3,Client,MacOSX,Database
The import process is pretty simple. I click a button, choose my csv file, it’s imported. More info on the operation of Parsets is here. A warning: I did have to revert back to version 2.0. Maybe Prof. Kosara could be convinced to allow downloads of 2.0.
I had to check and recheck the boxes on the left to get the data into the order I wanted. Here is what I got:
So who wants to show me their piechart that they think is perfectly capable of showing this??? Oh wait, PIE CHARTS WON’T DO THIS. Pie Charts can only show you one variable. This one has 4.
This is very similar to the parallel coordinate plot described by Stephen Few in Now You See It and shows Wilkinson’s example of analyzing who has reported defects. She was showing how to calculate a percentage for defects. See how the QA at the top is highlighted? There’s your percentage. Aside from who has reported the defects, Parsets makes it incredibly easy to see which OS has more defects and how the defects are spread out among the components. If I had more time, I would add a severity level to each defect. Wouldn’t that tell a story.
Parallel Sets is highly interactive. I can reorder the categories by checking and unchecking boxes. I can remove a category by unchecking a box if I wish.
By moving the mouse around, I can highlight and trace data points. Here I see that Defect 205 is a database defect for Mac OS X. Although I didn’t do it here, I bet that I could merge the Defect ID with a Defect Description and see both in the mouse over.
Parallel Sets is still pretty young, but is just so promising. I’m hoping that eventually, it will be viewable in a browser and easier to share. Visualizations like this one keep me engaged while providing me with useful information for exploratory analysis. That’s the promise of data viz, and Parallel Sets delivers.
Underpants Gnomes Among Us: Exploratory Analysis for Visualization and Testing
Sep 9th
Here’s a picture of tester dog, Laika, with Dr. James Whittaker’s new book, Exploratory Software Testing: Tips, Tricks, Tours, and Techniques to Guide Test Design.
Tester Dog
In reading through Stephen Few’s new book, Now You See It,
Stephen Few does a great job of explaining exploratory analysis with pictures:
Exploratory Analysis
Half of the people reading this now understand the underpants gnome tie-in. For those who don’t get it, here’s a link to the original South Park clip (NSFW).
Jokes aside, I’m going to start with the picture, and discuss what this says to me about testing and see if it meshes with what JW’s definition of exploratory testing. I will then look at how this applies to visualization. At the end, the two will either come together or not. At this point, I’m not sure if they will. I’ll just have to keep exploring until I have an answer or a comment telling me why my answer is crap (which is fine with me if you have a good point).
Starting with the picture and testing. I’m assuming the “?” means “write tests.” The eyeball means analyze. The light bulb is the decision of pass or fail. The illustration of directed analysis looks like the process HP Quality Center assumes. QC assumes you’ve primarily written tests and test steps before testing based on written requirements. Then you test. After you’ve tested, you have an outcome.
The second line for “exploratory” analysis looks like a much more cognitive and iterative process. This says that the tester has the opportunity to interact with the system-under-test (SUT) before formulating any tests(eyeball). After playing with the SUT, the tester pokes it with a few tests (“?”). At this point the tester may decide some stuff works and keep poking or decide that some stuff has failed and write defects(light bulb.) Chapter 2 of Exploratory Testing describes how JW defines exploratory testing: “Testers may interact with the application in whatever way they want and use the information the application provides to react, change course and generally explore the application’s functionality without restraint (16).” So far this is looking very similar.
Now that I’ve looked at how the exploratory analysis paradigm applies to testing, here’s how it applies to visualization. As an example visualization, I’m looking at a New York Times graphic, How Different Groups Spend their Day. When I open this graphic, I can see that it’s interactive, so I immediately slide my mouse across the screen. I notice the tool tips. Reading these gets me started reading the labels and eventually the description at the top. Then I start clicking. The boxes on the top right act as a filter. There is a also a filter that engages when a particular layer is clicked.
Few’s point in describing directed analysis vs. exploratory analysis is that in the wild, when we look at visualizations, we use exploratory analysis. It’s not like I knew what I was going to see before I opened the visualization. Few describes the process known as “Schneiderman’s mantra” (for Ben Schneiderman of treemap fame) in more detail saying that we make an overall assessment (eyeball), take a few specific actions (“?”), then reassess (eyeball). Although Few doesn’t say that there is a decision made at some point in this process, I’m assuming there is because of the light bulb in the picture (84).
Recently, Stephen Few asked for industry examples of people using visualization to do their work. Some of the replies were from the airline industry, a mail order warehouse and a medical center. Software engineers should be included in this mix and apparently from page 130 in JW’s book showing a treemap of Vista code complexity, already are. Given that both use the same form of exploratory analysis, I can see why.
Exploratory analysis of software testing and visualization diverge, however, when you look at the scale of data for which each is effective. Visualization requires a large dataset. This could be multiple runs of a set of tests or, as in JW’s example, analysis of large amounts of source code. Exploratory testing as JW describes can occur at a high level such as in the case of a visualization or at the level of an individual test.
One thing my exercise has shown me for sure is that I have to read more of Exploratory Testing.
Horizon Graphs for Code Churn
Sep 4th
My advisor has asked that I do some posts as a journal for my software visualization work this semester. Some will hopefully come to fruition before the end of the semester. Do or die time, right?
The first idea that I’ve tweeted about, talked about, cannot shut up about is using a horizon graph to depict code churn. Currently, most of my work has centered around treemaps, but they have a weakness. They only depict one moment in time. Since code churn is concerned with the number of changes over time, a treemap will only show you code churn up to a certain moment in time.
This example horizon graph on Stephen Few’s blog shows the stock price performance for 50 stocks over 1 years. An increase in color saturation indicates an increase up or down. The hue indicates whether the change is positive (blue) or negative (red).
Although the example is from Panopticon, I’m confident that Processing could produce similar results. The idea is really genius, and says very good things about Panopticon. I starred this a while ago in reader. Recently, Alan Page turned me on to some papers by an expert at Microsoft, Dr. Nachi Nagappan. I’ve been rooting around in some of them and noticed that code churn is mentioned again and again. I also noticed that he’s frequently working with millions of lines of code. I don’t know how many components that adds up to, but I’d like to see if the horizon graph would still be useful with hundreds of components, or if it would overwhelm the viewer.
If we’re going for a t.h.u.d, I think you’d have the treemap for upper levels in a hierarchy of components. If the user wanted to investigate an area more there would be a click through to something like the horizon graph.
Plagues aren’t just for blog posts
Aug 3rd
- Image via Wikipedia
For the past couple of months, James Whittaker has been writing about the “plagues of testing.” As he’s been posting, I’ve been reading through a book about a real plague.
As software testers, we see a system from a perspective that developers and business types rarely and may never see. We know our tests, we know how well they ran. We know our system under test and which components are picky. If you are like me, and have access to the code base, you also know the code. In the case of both the system and it’s code you know what should be better. Sometimes this is not a big deal, but sometimes it is a warning that it’s time to polish the resume. I have not seen this, personally, but I know that there are testers who find themselves in this position.
John Snow was a doctor in this position. He was an expert in the new art of anesthesiology in 1850′s London. He was also deeply involved in the study of cholera and concluded that it was a water born illness. This was very much counter to the prevailing theory of the time that cholera was passed through a sheer volume of stench or miasma (I can hear Dr. Evil saying this word). Health authorities in London, were so convinced that miasma, or extreme smellyness, was the reason for disease that they passed a law in 1848 requiring Londoners to drain their waste into a sewage system that would deposit into the Thames river. Unfortunately, the Thames was also a main source of drinking water for the city. Snow knew all of this and could see a health crisis in the making.
In 1854 when a major outbreak of cholera erupted in a neighborhood very close to where Snow lived, he conducted a thorough investigation in order to prove this theory that cholera was passed through water. He had a list that detailed the names and addresses of 83 people who had died from cholera. He also had an invaluable resource of detailed information about the neighborhood’s residents in the form of local clergyman Henry Whitehead. While Whitehead tracked down and questioned everyone he possibly could about their drinking habits, Snow analyzed this data to form patterns of who had been drinking the water, who had died and, just as importantly, who had NOT died. Not only were Snow and Whitehead able to convince the local parish board to remove the handle of a contaminated water pump, they knew their data so well that they were able to figure out the index, or original, case that had started the outbreak.
After the outbreak had subsided, Snow put the analysis from his investigation together with a very famous map into a monograph that circulated among London’s health professionals. His monograph slowly but effectively turned the tide of thinking among health professionals away from the miasma theory. This and a very smelly Thames convinced authorities to build a new sewer system that drained into the sea.
Frequently, when I talk to people about data visualization, they always ask me how I know what to visualize. The Ghost Map
Johnson and Whitehead could not drag the dead people’s relatives in front of London’s public health community and force the doctors to listen. These days doctor’s short attention span is because of insurance, but I’m sure there were other reasons back in the day. A good visualization does not take a long time for the viewer to process. That is their special power. Snow’s map is much more concise than a table of 83 names and addresses along with their individual stories. Visualization can quickly show your groups of tests that are failing. It can show that severe defects are increasing, and not decreasing, over time. Business may drive the ship/do not ship decision, but a good tester will know why a seriously ailing system is in so much trouble. A great tester can effectively communicate this to a business team.
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Submitted an Abstract to PNSQC
May 1st
I’m posting the abstract I just submitted to PNSQC. It’s also the abstract of the thesis I’m writing for my Masters. I’ve submitted a poster to the Grace Hopper Conference, but never before have I submitted a full-on paper requiring a full-on presentation. I chose PNSQC for 2 reasons: the focus is more on the practical side, unlike some of the ACM conferences and the conference is in Portland, Oregon. God, I love Portland.
Anyway, here is what I submitted:
Visualizing Software Quality
Moving quality forward will require better methods of assessing quality more quickly for large software systems. Assessing how much to test a software application is consistently a challenge for software testers especially when requirements are less than clear and deadlines are constrained.
For my graduate research and my job as a software tester, I have been looking at how visualization can benefit software testing. In assessing the quality of large-scale software systems, data visualization can be used as an aid. Visualizations can show complexity in a system, coverage of system or unit tests, where tests are passing vs. failing and which areas of a system contain the most frequent and severe defects.
In order to create visualizations for testing with a high level of utility and trustworthiness, I studied the principles of good data visualizations vs. visualizations with compromised integrity. Reading about these lead me to change some of the graphs that I had been using for my qa assessment and to adopt newer types of visualizations such as treemaps to show me where I should be testing and which areas of source code are more likely to have defects.
This paper will describe the principles of visualization I have been using, the visualizations I have created and how they are used as well as anecdotal evidence of their effectiveness for testing.
First Attempt at Visualizing Tests and Defects
Apr 24th
This post is about a visualization I created to show test execution status with related defects using data obtained from HP Quality Center. I’m using Excel to create this chart and deliberately stayed away from “fancy tricks.” If you want to recreate this some steps will be different if you don’t use Quality Center or Excel. In that case, you get to figure it out.
My weekly status meeting drove me to create this chart. Every week, I sit in this meeting with some pretty important people. Whenever I’m in a testing phase, I have to show what it is I’ve been working on. Previously, I’ve used the report templates from Quality Center, but they really are crap. Wait, that’s not big enough…They really are C-R-A-P. Not only is it difficult to jostle the correct data into place, but they are ugly and none of my superiors actually trusts the Quality Center visual. Since I’ve been doing all this reading about data viz, I realized that I could easily create something much better using Excel in less time.
There are several steps to creating any visualization, and I’ll break down the creation of this chart into the following steps. They are from Ben Fry’s excellent book, Visualizing Data: Exploring and Explaining Data with the Processing Environment
Acquire – getting your data
Parse – structuring, and sorting your data
Filter – remove stuff you don’t need from your data
Mine – apply mathemagic in the form of statistics, data mining, whatever to show patterns in your data
Represent – choose the type of graph or chart you will be using
Refine – polish your chart so that it has clarity and makes people want to look at it
Interact – add ways for the user to explore your chart
Acquire
Looking at your test set in quality center’s test lab, right click, and QC will show you an option at the bottom for “save as.” Click and this and choose excel sheet. Html would work too since excel can parse through QC’s html.
Parse
For this chart, I like to create 3 groupings: passed, failed and no run. You can have other groupings, but you probably want to make sure that the “other” groupings are together. In Excel, I sort the data by status into the 3 groups. If you are using a test set from the past, you might only have 2 groupings.
Filter
This will change depending on how large your team is and how the work is divided. Since I’m the only tester, we all know who executed the tests in my chart. I remove the columns for Attachment, Planned Host, Responsible Tester, Execution Date, Time and Subject. This leaves only 2 columns, Test Name and Status.
Mine
No mining in this one, it’s just straight up data.
Represent
This chart will be using a stacked bar format which looks similar to a stemplot, but isn’t really a stemplot.
Refine
Here’s where the magic is really happening. See that column for status? Add a column between “Test Name” and “Status.” Each cell in the new column gets a color according to that test’s status: Passed=green, Failed=red, No Run and anything else is gray. If you use the basic colors in Excel, your colors will be too bright. In Excel 2003 you can change this by navigating to Tools>Options>Colors. Here you can choose some less saturated versions of red and green. You’ll still need the very bright red, so don’t save another color over it.
Use fill color to change the colors in the empty column next to the status column. Now you can see each test and you can see how much is passed and how much is failed. Since the status is now indicated by color, you can get rid of the column with the status as text. If you’d rather keep it, you could have one column with the fill and the font color set the same. This would mask the text of the status inside the cell.
Gridlines can be very distracting, so clear all of them. To do this navigate to Tools>Options>View>Window options and clear the Gridlines check box. Doing this should immediately relax your eyes when you look at the chart.
To make it obvious which test has which status, right justify the column for test name.
If you want to show defects on your chart, you can do this using color. Remember where I said not to save over the bright red? If you have defects that you need to show on the chart, change the color of the related test case to bright red and add the I.D. and title of the defect to column on the right of the status. This works when you typically have 1 defect per test case. If you have more, I would just color that test bright red, and have a list of the defects elsewhere. Showing defects this way highlights the need for accurate and concise defect descriptions. I was reading in How We Test Software at Microsoft
Edward Tufte would probably say that I should print this chart out on a really big piece of paper, but he doesn’t work in my group. My status meeting is paperless, so this will be displayed on a big fancy conference room flat screen, ‘cause that’s how we roll. Actually, I’m not joking about the rolling part. The person who leads the meeting has a tendency to compulsively scroll up and down, so I designed my chart to be displayed within the width of his laptop screen. That’s why the summary is out to the side. Also, I made the text of the title and the summary much larger than the font of the individual tests. I’m using Trebuchet MS for the font.
Interact
At the most basic level, this chart allows the user to interact with the chart by focusing on the smaller, individual tests if they choose to do that. They can also look at the defect information if they would rather. As an improvement, I might figure out how to add some information for each test when it is moused over.
This chart probably won’t scale if you have multiple testers, multiple projects or copius amounts of tests and defects. I know that’s most testers, but I really made this chart specifically for my needs. This shows the very busy and important people what they need to see in a way that they can trust.
As always, comments are welcome. Love it, hate it? Let me know. I’m still learning about this stuff and welcome the feedback.
Integrity in Data Visualization: Part 2 of 2
Apr 11th
In the second part of this series about data visualization (Part 1 is here), I will show how, according to Edward Tufte, information visualizations can trick the viewer. Since I read through this chapter in his book, The Visual Display of Quantitative Information
Currently, I’m writing a paper for my metrics class on software visualization. After the class, it will be expanded to include how software visualization can work for testers. Part of knowing how to use visualization for any project is understanding what constitutes a good or bad visualization. I’m guessing that if you found my blog you might be a tester, and in that case, understanding the basics of data visualization will help you understand where I’m going with some of my upcoming posts. Outside of testing, understanding complex visualization is a skill we all need to have because we live in an age of data.
Labeling should be used extensively to dispel any ambiguities in the data. Explanations should be included for the data. Events happening within the data should be labeled.
For this example, I’d like to use a graphic that was in a post on TechCrunch as I was in the process of writing this post. It was posted by Vic Gundotra who is VP of Engineering for Mobile and Developer Products at Google. You can read his full post here. I’m calling out a couple of his graphics because I was pretty shocked that he would post these. I’ve seen some of his presentations on YouTube, and they were awesome presentations. Using graphics as bad as what he posted on TechCrunch only diminishes an otherwise strong message and will make me think twice about any visuals he presents in the future.
Here is the first of Vic Gundotra’s graphics. Notice how he does not label the totals on the graph, but separately at the bottom. There is no way a viewer can compare the data he’s representing.
In times series graphics involving money, monetary units should be standardized to account for inflation.
Ok, this might be a controversial example but please read the explanation before flaming me. I freaking love this award winning interactive graphic about movies created by the storied New York Times graphic department…BUT I have a bone to pick with it. The graphic shows movies from 1986 to 2008 and it does not account for inflation. There are some other things going on with this graphic as well, but since it’s about movies and not our budget crisis, I give it’s lack of adjustment for inflation a “meh.” It just goes to show that nothing is perfect.
All graphics must contain a context for the data they represent.
Here’s another Vic Gundotra graphic. Notice how there is no total for the number of users represented, yet Gundotra is trying to say that 20 times more are using the T-Mobile G1. By not including this number, Gundotra is not providing an accurate picture of how many people are using either. It could be 20 people using the G1 or it could be 20,000. There’s no way to know. The fact that he didn’t include this number says, to me, that maybe the number of people is embarrassingly small, but that’s another post for another blog.
Numbers represented graphically should be proportional to the numeric quantities they represent.
This is all about scale in graphics. If you are looking at a graphic, the pieces you are looking at should be to scale. Tufte actually has a formula for what he calls “The Lie Factor.” This link has a couple of illustrations and also shows how this formula is calculated.
Number of dimensions carrying information should not exceed dimensions in the data.
You know all of those 3d pie chart and bar graph templates in the Microsoft Excel chart wizard? Don’t use them anymore, and yes, I’ve used them myself in the not-too-distant past. They qualify as “chart junk” from Tufte’s perspective.
Variation should be shown for data only and not the design of the graphic.
I looked but couldn’t find a good example of this on the web. (If you see one let me know.) One of the graphs that Tufte uses to illustrate this point shows a bar graph where the bars for years that are deemed, “more relevant,” are popped out in a separate larger section using a really heinous 3d effect. It’s on page 63.
As always, comments are welcome.
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What is Data Visualization: Part 1 of 2 Characteristics of Excellent Visualizations
Mar 28th
In this post, I will be answering the question, “what is data visualization” and writing about some of Edward Tufte’s principles of for “excellent” data visualizations. This can be an aid in creating better graphs or in looking at graphs. In a subsequent posts, I will relate these fundamental principals to visualizations for use in software testing.
In his first book, The Visual Display of Quantitative Information
In the comments of my post “Exploring Data Visualization,” Eric asked me, “what is data visualization?” When I say data visualization I’m talking about a graphical depiction of statistical information that tells a story. These depictions can be simple or more complex, and they all have a point they are trying to make. According to Edward Tufte an excellent visualization expresses “complex ideas communicated with clarity, precision and efficiency,” (13).
To illustrate this have a look at one of my favorite interactive web graphics. “A Year of Heavy Losses,” from The New York Times. It illustrates the change in market capitalization of banks from 2007 to 2008. Be sure you click on the square at the top left to see the change. You can see not only the number of banks dwindling, but also their capitalization in the market. You can also mouse over each bank to see more granular data.
According to Tufte, these are some characteristics of excellent visualizations:
1. Lots of numbers packed into a tiny space
2. Data represented is not distorted
3. Extremely large data sets have coherency
4. Comparison between different pieces of data is easy
5. Data is revealed at a micro level and at a macro level
6. The data’s purpose is clear
7. Integration between the statistical and verbal descriptions of the data is tight
Here is an illustration Tufte uses as an example:
Train Schedule by E.J. Marey
It is a French train schedule from the 1880′s. Take some time to look at it and understand it, then look back at the characteristics I have just listed. Did you notice how the cities on the left are not listed at regular intervals? This is because Marey spaced them apart proportionately to their actual distance from each other. Since he did that, when you look at the slope of the lines, you are not only seeing arrival and departure times, but also the relative speed with which the train will get you from one place or the next. If you depend on trains to get you from one place to the next, this can be very important information.
This graphic also illustrates the concept of multivariate data which, according to Tufte is also a quality of excellent visualizations. I’m going to break out what’s in the train illustration into univariate, bivariate and multivariate data. If I miss something, just add a comment.
Let’s start with the concept behind this illustration. It’s depicting arrival times and departure times of trains in France. It shows the route the trains take, and the relative speed with which they make from one station to the next.
Univariate data shows the frequency/probability of one variable.
Some univariate data from this graphic: the number of trains arriving or departing a station. The number of trains arriving at stations at any one time. The number of arrivals at a station each day. The number of departures from Chagny station each day. Each of the variables I have described is a frequency (Head First Statistics 609).
Bivariate data shows 2 values for an observation.
Bivariate data from this graphic:
(x) Time of day
(y) Number of trains arriving/departing at Chagny station
For this observation you need two variables(Head First Statistics 610).
Multivariate data shows multiple values for an observation.
If we take the observation from the bivariate data example and add stations, the observation becomes multivariate and is what you see in Marey’s illustration.
I’ve just covered a lot of material and I hope it gives you a good idea of what data visualization and the field of information visualization is all about. In my next post, I’ll be covering the ways in which graphs can lie. I’ve seen this happen at work and just completed a reading assignment for school where it was also an issue. These are complicated topics that software engineers should understand if they are to use visualization in ways such as a tester’s heads up display.
Questions and comments are always welcome.
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Exploring Data Visualization
Mar 25th
For the past few months I’ve been obsessively learning about data visualization so I’m posting about my exploration with links to everything (books, blogs, graphics, people, etc.) This topic fascinates because it brings together all of my studies including art, art history, theatrical design, computer science and software engineering.
Last fall, I found the book Visualizing Data: Exploring and Explaining Data with the Processing Environment
So last Fall, I was having fun with these examples, and then I went to GTAC. I know I’ve already written about James Whittaker’s keynote, but just bear with me. Seeing how transfixed the crowd was with the few data visualizations he uses for testing, I felt something click in my head. There aren’t many moments in life when we get total clarity, but I finally had a huge one and decided not to let it go.
Before, I had just been playing with data visualization and happy that it fed my artistic side, but now, I was in it for keeps. When I came home, I looked at the other books that Ben Fry was referencing and found the ultimate classic of data visualization. If you only ever read one book about this topic, that book should be Edward Tufte’s The Visual Display of Quantitative Information, 2nd edition
Please note that Tufte will not tell you what type of graph to use in any particular situation. For that, I turned to Head First Statistics
Since blogging is such a great font of information, I went out looking for blogs and found several that I really enjoy. There are definitely other worthwhile blogs on data visualization.These are the ones I’m reading regularly:
Jorge Camoes’ Charts
Visual Business Intelligence (Stephen Few’s blog)
Excel Charts and Tutorials by Peltier Technical Services
Information Ocean
A few weeks ago, Edward Tufte offered a seminar in Atlanta, and I was fortunate enough to go. It’s pricey, but you get all four of his lovely hardback books are included which somewhat offsets the cost of admission. I found some excellent notes that were taken a few days later in Raleigh on Justin Wehr’s blog. I could tell that Dr. Tufte had given his prezo a few (hundred) times, but seeing him present his material provoked some really deep thinking. When the presentation was over, I walked to a bench in the hotel lobby, and put together the bones of my thesis. Visionaries such as Dr. Tufte always inspire my best thinking.
Currently, I’m reading through lots of research papers about the visualization of source code. I’ll make a separate blog post for that. Well, there might be several separate blog posts for that. For the first time in my life, I feel completely engaged in what I’m doing at work and at school.