This paper made two main contributions: it defined system dependence graphs (SDGs), which extended program dependence graphs (PDGs) [31,17] to incorporate collections of procedures (with procedure calls) rather than just monolithic programs. It then defined an interprocedural slicing algorithm that identifies the components of the SDG that might affect the values of the variables defined at or used at a given program point Ô, and/or how often Ô executes. The novelty of the algorithm was that it correctly accounts for the calling context of a called procedure, thereby improving on the technique for interprocedural slicing algorithm that had been presented in Mark Weiser's seminal papers on program slicing [47,48].

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Bad code smells have been defined as indicators of potential problems in source code. Techniques to identify and mitigate bad code smells have been proposed and studied. Recently bad test code smells (test smells for short) have been put forward as a kind of bad code smell specific to tests such a unit tests. What has been missing is empirical investigation into the prevalence and impact of bad test code smells. Two studies aimed at providing this missing empirical data are presented. The first study finds that there is a high diffusion of test smells in both open source and industrial software systems with 86 % of JUnit tests exhibiting at least one test smell and six tests having six distinct test smells. The second study provides evidence that test smells have a strong negative impact on program comprehension and maintenance. Highlights from this second study include the finding that comprehension is 30 % better in the absence of test smells

Readers of programs have two main sources of domain information: identifier names and comments.When functions are uncommented, as many are, comprehension is almost exclusively dependent on the identifier names. Assuming that writers of programs want to create quality identifiers (e.g., include relevant domain knowledge) how should they go about it? For example, do the initials of a concept name provide enough information to represent the concept? If not, and a longer identifier is needed, is an abbreviation satisfactory or does the concept need to be captured in an identifier that includes full words?Results from a study designed to investigate these questions are reported. The study involved over 100 programmers who were asked to describe twelve different functions. The functions used three different "levels" of identifiers: single letters, abbreviations, and full words. Responses allow the level of comprehension associated with the different levels to be studied.The functions include standard algorithms studied in computer science courses as well as functions extracted from production code. The results show that full word identifiers lead to the best comprehension; however, in many cases, there is no statistical difference between full words and abbreviations.

Current slicing techniques cannot handle systems written in multiple programming languages. Observation-Based Slicing (ORBS) is a language-independent slicing technique capable of slicing multilanguage systems, including systems which contain (third party) binary components. A potential slice obtained through repeated statement deletion is validated by observing the behaviour of the program: if the slice and original program behave the same under the slicing criterion, the deletion is accepted. The resulting slice is similar to a dynamic slice. We evaluate five variants of ORBS on ten programs of different sizes and languages showing that it is less expensive than similar existing techniques. We also evaluate it on bash and four other systems to demonstrate feasible large-scale operation in which a parallelised ORBS needs up to 82% less time when using four threads. The results show that an ORBS slicer is simple to construct, effective at slicing, and able to handle systems written in multiple languages without specialist analysis tools.