Large project overruns and overtime work have been reported in the software industry. Experiments and case studies have investigated the relationship between time pressure and software quality and productivity. Our search strategy examined 5,332 papers and identified 88 papers as having relevant contributions related to time pressure in a software engineering. Our review investigated definitions, metrics, and causes of time pressure. Also, we map the papers to process phases and approaches. Last, we summarize the effects of time pressure on quality and productivity. The majority of the reported results support the outcome of reduced quality and increased productivity with time pressure.

Background: Unstructured and textual data is increasing rapidly and Latent Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods for it. Past work suggests that instability of LDA topics may lead to systematic errors. Aim: We propose a method that relies on replicated LDA runs, clustering, and providing a stability metric for the topics. Method: We generate k LDA topics and replicate this process n times resulting in n*k topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k clusters now represent the original LDA topics and we present them like normal LDA topics showing the ten most probable words. For the clusters, we try multiple stability metrics, out of which we recommend Rank-Biased Overlap, showing the stability of the topics inside the clusters. Results: We provide an initial validation where our method is used for 270,000 Mozilla Firefox commit messages with k=20 and n=20. We show how our topic stability metrics are related to the contents of the topics. Conclusions: Advances in text mining enable us to analyze large masses of text in software engineering but non-deterministic algorithms, such as LDA, may lead to unreplicable conclusions. Our approach makes LDA stability transparent and is also complementary rather than alternative to many prior works that focus on LDA parameter tuning.

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Ad hoc networks are self-organizing wireless systems conformed by cooperating neighboring nodes that conform networks with variable topology. Analyzing these networks is a complex task due to their dynamic and irregular nature. Cellular Automata (CA), a very popular technique to study self-organizing systems, can be used to model and simulate ad hoc networks, as the modeling technique resembles the system being modeled. Cell-DEVS was proposed as an extension to CA in which each cell in the system is considered as a DEVS model. The approach permits defining models with asynchronous behavior, and to execute them with high efficiency. We show how these techniques can be used to model mobile wireless ad hoc networks, making easy model definition, analysis and visualization of the results. The use of Cell-DEVS permitted us to easily develop new experiments, which allowed us to extend routing techniques for inter-networking and multicast routing, while permitting seamless integration with traditional networking models.