Modern society is irreversibly dependent on computers and, consequently, on software. However, as the complexity of programs increase, so does the number of defects within them. To alleviate the problem, automated techniques are constantly used to improve software quality. Static analysis is one such approach in which violations of correctness properties are searched and reported. Static analysis has many advantages, but it is necessarily conservative because it symbolically executes the program instead of using real inputs, and it considers all possible executions simultaneously. Being conservative often means issuing false alarms, or missing real program errors. Pointer variables are a challenging aspect of many languages that can force static analyis tools to be overly conservative. It is often unclear what variables are affected by pointer-manipulating expressions, and aliasing between variables is one of the banes of program analysis. To alleviate that, a common solution is to allow the programmer to provide annotations such as declaring a variable as unaliased in a given scope, or providing special constructs such as the “never-null” pointer of Cyclone. However, programmers rarely keep these annotations up-to-date. The solution is to provide some form of pointer analysis, which derives useful information about pointer variables in the program. An appropriate pointer analysis equips the static tool so that it is

Abstract—Automated design pattern detection (DPD) is a challenging reengineering task that has been shown to require combination of complex structural and behavioural analyses for good results. Still, the detection quality (precision and recall) of existing tools has so far been insufficient to make DPD integral part of current IDEs and development practices. In this paper we present a novel approach and a related tool, DPJF. For all implemented pattern detectors and all projects used for evaluation, DPJF provides competitive performance while achieving the best recall of all evaluated tools (with a median of 89%) and 100 % precision. These results lay the basis for routine application of DPD in program comprehension and software quality assesment. The high detection quality is achieved by a well-balanced combination of structural and behavioural analysis techniques whereas the good performance is achieved by emprically validated simplifications of the individual techniques. I.