We propose a tool for inferring transformation specifications from a few examples of original and updated code. These transformation specifications may contain multiple code fragments from within a single function, all of which must be present for the transformation to apply. This makes the inferred transformations context sensitive. Our algorithm is based on depth-first search, with pruning. Because it is applied locally to a collection of functions that contain related changes, it is efficient in practice. We illustrate the approach on an example drawn from recent changes to the Linux kernel.

A key issue in maintaining Linux device drivers is the need to update drivers in response to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes made in a set of pairs of files and their updated versions, and extracts a generic patch performing those changes. Library developers can use our tool to extract a generic patch based on the result of manually updating a few typical driver files, and then apply this generic patch to other drivers. Driver developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect to the changes performed in the provided pairs of driver files.

The rapid detection and treatment of bugs in operating systems code is essential to maintain the overall security and dependability of a computing system. A number of techniques have been proposed for detecting bugs, but little has been done to help developers analyze and treat them. In this paper we propose to combine bug-finding rules with transformations that automatically introduce bug-fixes or workarounds when a possible bug is detected. This work builds on our previous work on the Coccinelle tool, which targets device driver evolution.

Analyzing data obtained from web server logs, so-called "clickstreams", is rapidly becoming one of the most important activities for companies in any sector as most businesses become ebusinesses.Clickstream analysis can reveal usage patterns on the company's web site and give a highly improved understanding of customer behavior.This understanding can then be utilized for improving customer satisfaction with the web site and the company in general, yielding a huge business advantage.In this paper, we present the results of a clickstream analysis project at a large Danish mortgage provider.The paper first describes clickstream data and its usefulness, then it introduces the questions that the company wanted answered in the project.One of the major problems in clickstream analysis is sequences of clicks, which are difficult to handle using normal techniques.This problem is handled by introducing the concept of subsessions, which captures sequences of clicks explicitly.Techniques for overcoming the potential explosion in the number of subsessions and for filtering out unnecessary web requests are presented and the effectiveness of the techniques is evaluated.The proposed approach has been successfully implemented and tested and is currently being integrated in the company's web system architecture.