Uncovering Dependence Clusters and Linchpin Functions

Dependence clusters are (maximal) collections of mutually dependent source code entities according to some dependence relation. Their presence in software complicates many maintenance activities including testing, refactoring, and feature extraction. Despite several studies finding them common in production code, their formation, identification, and overall structure are not well understood, partly because of challenges in approximating true dependences between program entities. Previous research has considered two approximate dependence relations: a fine-grained statement-level relation using control and data dependences from a program’s System Dependence Graph and a coarser relation based on function-level controlflow reachability. In principal, the first is more expensive and more precise than the second. Using a collection of twenty programs, we present an empirical investigation of the clusters identified by these two approaches. In support of the analysis, we consider hybrid cluster types that works at the coarser function-level but is based on the higher-precision statement-level dependences. The three types of clusters are compared based on their slice sets using two clustering metrics. We also perform extensive analysis of the programs to identify linchpin functions – functions primarily responsible for holding a cluster together. Results include evidence that the less expensive, coarser approaches can often be used as e�ective proxies for the more expensive, finer-grained approaches. Finally, the linchpin analysis shows that linchpin functions can be e�ectively and automatically identified.

The effect of carbohydrate mouth rinse on a 30-minute arm cranking performance

The aim of this study was to examine the effect of carbohydrate mouth rinse on 30-min arm cranking performance. Twelve healthy, active males (age 21.6, standard deviation (SD)=3.1 years; mass 76.2, SD=12.2 kg) volunteered in a single-blind, randomised crossover design. Firstly they completed an incremental exercise test to exhaustion (VO2max test) on an arm crank (50W for 2 min, increasing by 10W every min). During visit 2 and 3 they arm cranked for maximal distance over 30 min at a resistance equivalent to 50% of their peak power, mouth rinsing for 5 s with either 25 ml of a tasteless 6.4% maltodextrin solution (CHO) or 25 ml of water (placebo) every 6 min. A letter cancellation test was performed pre and post exercise to measure cognitive function. The result showed that cognitive function was not significantly different between trials (P=0.874). There was no significant difference in distance arm cranked between trials (P=0.164) even though 9 out of 12 participants had improved performance on the CHO trial. In conclusion, further research is needed to determine the ergogenic effect of CHO mouth rinsing on upper body exercise performance.

Clustering of lifestyle risk behaviours among residents of forty deprived neighbourhoods in London: lessons for targeting public health interventions

Background Clustering of lifestyle risk behaviours is very important in predicting premature mortality. Understanding the extent to which risk behaviours are clustered in deprived communities is vital to most effectively target public health interventions. Methods We examined co-occurrence and associations between risk behaviours (smoking, alcohol consumption, poor diet, low physical activity and high sedentary time) reported by adults living in deprived London neighbourhoods. Associations between sociodemographic characteristics and clustered risk behaviours were examined. Latent class analysis was used to identify underlying clustering of behaviours. Results Over 90% of respondents reported at least one risk behaviour. Reporting specific risk behaviours predicted reporting of further risk behaviours. Latent class analyses revealed four underlying classes. Membership of a maximal risk behaviour class was more likely for young, white males who were unable to work. Conclusions Compared with recent national level analysis, there was a weaker relationship between education and clustering of behaviours and a very high prevalence of clustering of risk behaviours in those unable to work. Young, white men who report difficulty managing on income were at high risk of reporting multiple risk behaviours. These groups may be an important target for interventions to reduce premature mortality caused by multiple risk behaviours.