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Context. Several empirical studies have explored the benefits of software design patterns, but their collective results are highly inconsistent. Resolving the inconsistencies requires investigating moderators-i.e., variables that cause an effect to differ across contexts. Objectives. Replicate a design patterns experiment at multiple sites and identify sufficient moderators to generalize the results across prior studies. Methods. We perform a close replication of an experiment investigating the impact (in terms of time and quality) of design patterns (Decorator and Abstract Factory) on software maintenance. The experiment was replicated once previously, with divergent results. We execute our replication at four universities-spanning two continents and three countries-using a new method for performing distributed replications based on closely coordinated, small-scale instances ("joint replication"). We perform two analyses: 1) a post-hoc analysis of moderators, based on frequentist and Bayesian statistics; 2) an a priori analysis of the original hypotheses, based on frequentist statistics. Results. The main effect differs across the previous instances of the experiment and across the sites in our distributed replication. Our analysis of moderators (including developer experience and pattern knowledge) resolves the differences sufficiently to allow for cross-context (and cross-study) conclusions. The final conclusions represent 126 participants from five universities and twelve software companies, spanning two continents and at least four countries. Conclusions. The Decorator pattern is found to be preferable to a simpler solution during maintenance, as long as the developer has at least some prior knowledge of the pattern. For Abstract Factory, the simpler solution is found to be mostly equivalent to the pattern solution. Abstract Factory is shown to require a higher level of knowledge and/or experience than Decorator for the pattern to be beneficial.

We present the results of a study in which author entropy was used to characterize author contributions per file. Our analysis reveals three patterns: banding in the data, uneven distribution of data across bands, and file size dependent distributions within bands. Our results suggest that when two authors contribute to a file, large files are more likely to have a dominant author than smaller files.

Programmers often develop software in multiple languages. In an effort to study the effects of programming language fragmentation on productivity—and ultimately on a developer’s problem-solving abilities—the authors present a metric, language entropy, for characterizing the distribution of a developer’s programming efforts across multiple programming languages. This paper presents an observational study examining the project contributions of a random sample of 500 SourceForge developers. Using a random coefficients model, the authors find a statistically (alpha level of 0.001) and practically significant correlation between language entropy and the size of monthly project contributions. Results indicate that programming language fragmentation is negatively related to the total amount of code contributed by developers within SourceForge, an open source software (OSS) community.

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