Software testing has no boundaries at all. This discipline is so unique that it’s not very common to see systematic approaches due to the variety of material and the changing tradeoffs. A few weeks ago, I came across to a decent software testing article from a Microsoft engineer which was published on Live Spaces. Unfortunately, it was followed by 2 spam comments — was very ironic to see such an assertive article was ruined by two regular Russian spammers.
I love machine learning and classification. My whole life is being spent between two parameters: accuracy and precision. These are the common statistical values to determine how successful your system is. If you have a search engine, accuracy may tell you what percentage of retrieved documents are really relevant. And percentage is a value to determine how likely your results cover all the relevant documents available.
Surprisingly a few days ago, I was asked to break a machine learning system during a job interview. I was asked to come up with some possible cases. According to my own philosophy, accuracy and precision are parts of the system requirements. They are related with the quality of the overall product. But how are you going to collect information to come up with these numbers? Imagine you are working on a searching engine. Is it manageable to find n people and ask them manually if they like the results or not? Will your sample (n people) reflect your user base? How costly it will be and how objective? Is it really scalable? Is it possible to for a human to read all of the documents on the Web and decide which are really related to his search phrase? These are a few introductory level problems with analysis of accuracy and precision.
Post-Processing and the Importance of Feedback
It may not be critical for you to release a product with a target accuracy and precision. Mostly, consumer market suits this model the best. But this alone should not be translated into the “inessentiality of the quality tracking”. I am just advising you to track the quality after the release (similar to ship-then-test method). Detect which results are exit-links, provide instant feedback tools for users to relocate their results and etc. Use acquired feedback to improve the existing system. Testing may not be done with the release, you may need to discuss and analyze if your product is performing well and report to your development team and influence them with scalable user-oriented improvements.
Burcu Dogan 