Talk Nerdy To Me

The technology behind Secure Sockets Layer (SSL) network connections is often perceived as a bit of “black magic” – smoke and mirrors securing our Internet connections from snooping.  When banking and shopping online, even a novice user understands their browser sets up an HTTPS connection (which is simply HTTP over SSL) to protect the transaction.  It’s easy to simply surf to a secure URL and know that, somehow, SSL is magically keeping you safe.

Developing software that uses SSL is an entirely different matter.  The simplicity quickly fades, and the developer must confront the complexities of certificate management, trust stores, handshaking, and a host of other details that must be perfectly aligned to make the secure communication work.  In Part 1, we’ll cover a very high level of SSL concepts.  In subsequent posts, we’ll take a deeper dive into making these connections happen in both Java and C#.

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Early PCs seldom had more than a tiny, weak fan on the back of the case to push out excess heat generated by the internal electronics.  As transistors shrank and chips grew faster and more complex, CPUs began running hotter and reaching dangerous temperatures - so hot, in fact, that the little case fan couldn’t protect the delicate electronics from burning out.

To address this, PC manufacturers began adding fans dedicated to cooling this nerve center of the motherboard.  Today, with high end gaming machines consuming up to 1000W or more, enormous heat is generated not just by the CPU, but by the memory, north and south bridges, and the graphics card.  To expel this heat from inside the case, larger and faster case fans are needed to keep everything running at a safe, relatively cool temperature.

For the past few years, PC accessory vendors have been marketing liquid cooling systems.  These products promise to cool more efficiently, and more quietly, than traditional fans - at the same time adding several hundred dollars to the total price tag of a new machine.  The question is: is this just a pricey gimmick, or is this the next logical step in the progression of ever more powerful machines?

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A great Open Source project for gaining understanding about e-mail systems, including an in-depth look at SMTP and POP3, is the Java-based Apache JAMES Project.  Although JAMES has the unfortunate shortcoming of being built around the now defunct and unsupported Apache Avalon Framework, it’s still a fantastic learning tool for understanding email protocols, mail delivery, and spam filtering.  Not only that, it’s a fully functional, enterprise-ready mail server that can be up and running with minimal configuration.

One technology implemented by JAMES for spam filtering is real-time DNS blacklists.  DNSBLs identify the IP addresses of potential spam sources and machines known to be delivering spam (as determined by the sometimes controversial policies of the list owner).  Spam blacklists date back to 1996 with Paul Vixie’s Mail Abuse Prevention System, and are now used by ISPs and corporate mail systems around the world.  Countless organizations maintain blacklists, and Web sites like MX Toolbox permit ad hoc queries of IP addresses against dozens of published lists.

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Yet another software development disaster is headed for the digital trash heap of failed projects. This time, the casualty is software funded by the U. S. Census Bureau. The Associated Press reports failure to deliver usable software to census enumerators could add as much as $2 billion to the 2010 census. Worse, the AP reports “census officials are considering a return to using paper and pencil to count every man, woman and child in the nation.”

This is a spectacular train wreck that had doom written all over it from Day One. It’s a familiar, predictable pattern constantly repeated since the first clueless manager commanded “just make it user friendly”.

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JUnit Factory is rather clever how it analyzes and executes your code to generate characterization tests. However, legacy Java code was generally not written with testability in mind. This sometimes makes it difficult for JUnit Factory to attain complete coverage of your code due to the need for objects to exist in a complex state or the need to interact with an external resource such as a database.

JUnit Factory is often able to generate mock instances automatically for problematic classes. When automocking fails, the developer can improve coverage by either extracting behaviors into private methods or by providing hints to JUnit Factory in the form of test data helpers.

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Characterization tests provide a safety net for your legacy Java code by helping identify unintended changes in software behavior caused by code maintenance. JUnit Factory from Agitar Software may be used to automatically generate these tests for you. In this post, we’ll take a look at what happens to these characterization tests when a simple code change is made.

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JUnit Factory is a free Eclipse plug-in from Agitar Software that generates characterization tests for your Java code. For more background on what characterization tests are, and how you use them, you’ll want read my post “Characterization Tests: How To Deal With Legacy Java Code”.

This article describes how to generate tests for a simple Java class and how to read the tests. Not all of your real code will be this simple, and not all generated tests will be this simple, either. But, bear with me as we start small and work our way up.

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Companies have invested billions of dollars over the last decade building components and applications based on the Java framework. This work represents a wealth of expertise and collective knowledge that firms must protect and maintain. Unfortunately, in the dynamic field of software development where programmers change jobs, on average, every 18 months, the original developers on these past projects probably aren’t around anymore.

As a result, Java developers seldom have the luxury of working on true greenfield projects. Instead, they are faced with adding enhancements and fixing bugs on projects built upon a code base they didn’t write and don’t fully understand. How can developers safely make changes to legacy code without accidentally breaking something unrelated?

Characterization tests provide a safety net – a change detection engine – that identifies behavioral changes in legacy code in order to remedy regressions early in the development process. Fixing regressions early shortens development timelines, increases code quality, and allows a team to become more agile. You can automatically generate your team’s characterization tests using the free JUnit Factory for Java from Agitar Software.

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