Configurable software systems enable developers to configure at compile time a single variant of the system to tailor it towards specific environments and features. Although traditional static analysis tools can assist developers in software development and maintenance, they can only run on a concrete configuration of a configurable software system. Thus, it is necessary to derive many configurations so that the configuration-specific parts of the source code can be checked. To avoid this tedious and error-prone process, we propose an approach to automatically derive a set of configurations that cover as many combinations of configuration-specific blocks of code or source files as possible. We represent a C program with CPP directives (e.g., #ifdef) with a CPP control-flow graph (CPP-CFG) in which CPP expressions are condition nodes and #ifdef blocks are statement nodes. We then explore possible paths on CPP-CFG with dynamic symbolic execution and depth-first search algorithms, and correspondingly, producing possible combinations of concrete blocks of C code, on which an existing static analysis tool can run. Our preliminary evaluation on a benchmark of configuration-dependent bugs on Linux shows that our approach can detect more bugs than a state-of-the-art tool.

A kernel oops is an error report that logs the status of the Linux kernel at the time of a crash. Such a report can provide valuable first-hand information for a Linux kernel maintainer to conduct postmortem debugging. Recently, a repository has been created that systematically collects kernel oopses from Linux users. However, debugging based on only the information in a kernel oops is difficult. We consider the initial problem of finding the offending line, i.e., the line of source code that incurs the crash. For this, we propose a novel algorithm based on approximate sequence matching, as used in bioinformatics, to automatically pinpoint the offending line based on information about nearby machine-code instructions, as found in a kernel oops. Our algorithm achieves 92% accuracy compared to 26% for the traditional approach of using only the oops instruction pointer.

Mediated schemas lie at the center of the well recognized data integration architecture. Classical data integration systems rely on a mediated schema created by human experts through an intensive design process. Automatic generation of mediated schemas is still a goal to be achieved. We generate mediated schemas by merging multiple source schemas interrelated by tuple-generating dependencies (tgds). Schema merging is the process to consolidate multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autonomous. Existing approaches fall short in various aspects, such as restricted expressiveness of input mappings, lacking data level interpretation, the output mapping is not in a logical language (or not given at all), and being confined to binary merging. We present here a novel system which is able to perform native n-ary schema merging using P2P style tgds as input. Suited in the scenario of generating mediated schemas for data integration, the system opts for a minimal schema signature retaining all certain answers of conjunctive queries. Logical output mappings are generated to support the mediated schemas, which enable query answering and, in some cases, query rewriting.