This special issue introduces current developments in bioinformatics, the use of computational techniques in the service of the life sciences, in the specific context of the analysis and interpretation of DNA molecules, where DNA is the primary informational molecule of all life. To fully appreciate the papers in this issue, some background knowledge in biology is required. The DNA in a cell is organized into a genome consisting of one or more chromosomes, each of which is a DNA molecule, which can be thought of as a long string over the alphabet of nucleotides, A, C, G, and T. Within a DNA string or sequence are substrings that are genes that normally encode the recipe for the functional proteins in the cell. The human genome is organized into 23 pairs of chromosomes, one maternal and one paternal chromosome set, containing a total of approximately six billion nucleotides. Amazingly, all ~1 trillion cells in one’s body, of diverse form and function, contain the same “molecular blueprint” with a near perfect copy of their genome.

We present PEAT: the Portable Executable Analysis Toolkit. It is a software prototype designed to provide a selection of tools that an analyst may use in order to examine structural aspects of a Windows Portable Executable (PE) file, with the goal of determining whether malicious code has been inserted into an application after compilation. These tools rely on structural features of executables that are likely to indicate the presence of inserted malicious code. The underlying premise is that typical application programs are compiled into one binary, homogeneous from beginning to end with respect to certain structural features; any disruption of this homogeneity is a strong indicator that the binary has been tampered with. For example, it could now harbor a virus or a Trojan horse program. We present our investigation into structural feature analysis, the development of these ideas into the PEAT prototype, and results that illustrate PEAT's practical effectiveness.

This paper discusses the use of genetic algorithms (GAs) for automatic software test data generation. This research extends previous work on dynamic test data generation where the problem of test data generation is reduced to one of minimizing a function. In our work, the function is minimized by using one of two genetic algorithms in place of the local minimization techniques used in earlier research. We describe the implementation of our GA-based system and examine the effectiveness of this approach on a number of programs, one of which is significantly larger than those for which results have previously been reported in the literature. We also examine the effect of program complexity on the test data generation problem by executing our system on a number of synthetic programs that have varying complexities.

In software testing, it is often desirable to find test inputs that exercise specific program features. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, numerous attempts have been made to automate the process. Random test data generation consists of generating test inputs at random, in the hope that they will exercise the desired software features. Often, the desired inputs must satisfy complex constraints, and this makes a random approach seem unlikely to succeed. In contrast, combinatorial optimization techniques, such as those using genetic algorithms, are meant to solve difficult problems involving the simultaneous satisfaction of many constraints. In this paper, we discuss experiments with a test generation problem that is harder than the ones discussed in earlier literature-we use a larger program and more complex test adequacy criteria. We find a widening gap between a technique based on genetic algorithms and those based on random test generation.