Large software companies need customized tools to manage their source code. These tools are often built in an ad-hoc fashion, using brittle technologies such as regular expressions and home-grown parsers. Changes in the language cause the tools to break. More importantly, these ad-hoc tools often do not support uncommon-but-valid code code patterns. We report our experiences building source-code analysis tools at Google on top of a third-party, open-source, extensible compiler. We describe three tools in use on our Java code base. The first, Strict Java Dependencies, enforces our dependency policy in order to reduce JAR file sizes and testing load. The second, error-prone, adds new error checks to the compilation process and automates repair of those errors at a whole-code base scale. The third, Thindex, reduces the indexing burden for a Java IDE so that it can support Google-sized projects.

In this paper, we consider the problem of throughput maximization in an infrastructure based WLAN. We demonstrate that most of the proposed protocols though perform optimally for connected network (no hidden terminals), their performance is worse than even that of standard IEEE 802.11 in presence of hidden terminals. Here we present a stochastic approximation based algorithm that not only provide optimum throughput in a fully connected network but also when hidden nodes are present.