You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Bug finding tools can find defects in software source code using an automated static analysis. This automation may be able to reduce the time spent for other testing and review activities. For this we need to have a clear understanding of how the defects found by bug finding tools relate to the defects found by other techniques. This paper describes a case study using several projects mainly from an industrial environment that were used to analyse the interrelationships. The main finding is that the bug finding tools predominantly find different defects than testing but a subset of defects found by reviews. However, the types that can be detected are analysed more thoroughly. Therefore, a combination is most advisable if the high number of false positives of the tools can be tolerated.

Cited Abstract(s):
[1]: Bugs in software are costly and difficult to find and fix. In recent years, many tools and techniques have been developed for automatically finding bugs by analyzing source code or intermediate code statically (at compile time). Different tools and techniques have different tradeoffs, but the practical impact of these tradeoffs is not well understood. In this paper, we apply five bug finding tools, specifically Bandera, ESC Java 2, FindBugs, JLint, and PMD, to a variety of Java programs. By using a variety of tools, we are able to cross-check their bug reports and warnings. Our experimental results show that none of the tools strictly subsumes another, and indeed the tools often find nonoverlapping bugs. We discuss the techniques each of the tools is based on, and we suggest how particular techniques affect the output of the tools. Finally, we propose a meta-tool that combines the output of the tools together, looking for particular lines of code, methods, and classes that many tools warn about.
In [1] among others PMD and FindBugs are compared based on their warnings which were not all checked for false positives. The findings are that although there is some overlap the warnings generated by the tools are mostly distinct. We can support this result with our data.