Daniel Spiewak and Aaron Bedra take a look at code verifying starting with Tony Hoare’s paper on testing(1969), type theory, and language-integrated proof systems.
William Pugh explains how to use FindBugs, a Java static code analysis tool, to discover bugs. The talk covers general issues regarding code bugs with advice on how to make sure you get rid of them.
Erik Dörnenburg shares techniques for estimating code quality by collecting and analyzing data using the toxicity chart, metrics tree maps, size&complexity pyramid, complexity view, code city, etc.
Michael Feathers analyzes real code bases concluding that code is not nearly as beautiful as designers aspire to, discussing the everyday decisions that alter the code bit by bit.
Bernhard Merkle advices on preventing architectural degradation of a project by using tools for constant monitoring of the code, exemplifying with an analysis of Ant, Findbugs and Eclipse.
Erik Dörnenburg explains how to use various visualization tools to spot patterns, trends and outliers in the code that are an indication of code quality level.
Cliff Click discusses the Von Neumann architecture, CISC vs RISC, Instruction-Level Parallelism, pipelining, out-of-order dispatch, cache misses, memory performance, and tips to improve performance.
Magnus Robertsson shows how to control the code architecture to avoid an architectural drift leading to a big-ball-of-mud: peer review, code analysis, and zero tolerance to warnings and errors.
Charles Nutter discusses bringing JRuby to the JVM, why Ruby is hard to implement, JIT compilation, precompilation, core Ruby implementation, Java library access, library challenges and future plans.
In this RubyFringe talk, Reginald Braithwaite writes Ruby code to read, write, and rewrite Ruby. Demos include extending Ruby with conditional expressions, call-by-name and more.
Cliff Click discusses how to optimize generated bytecode for running on the JVM. Click analyzes and reports on several JVM languages and shows several places where they could increase performance.
Creating secure code requires more than just good intentions. Static source code analysis can be used to uncover the kinds of errors that lead directly to vulnerabilities. Brian Chess shows you how.