An Empirical Study of Cohesion and Coupling: Balancing Optimisation and Disruption

Search based software engineering has been extensively applied to the problem of finding improved modular structures that maximise cohesion and minimise coupling. However, there has, hitherto, been no longitudinal study of developers’ implementations, over a series of sequential releases. Moreover, results validating whether developers respect the fitness functions are scarce, and the potentially disruptive effect of search-based remodularisation is usually overlooked. We present an empirical study of 233 sequential releases of 10 different systems; the largest empirical study reported in the literature so far, and the first longitudinal study. Our results provide evidence that developers do, indeed, respect the fitness functions used to optimise cohesion/coupling (they are statistically significantly better than arbitrary choices with p << 0.01), yet they also leave considerable room for further improvement (cohesion/coupling can be improved by 25% on average). However, we also report that optimising the structure is highly disruptive (on average more than 57% of the structure must change), while our results reveal that developers tend to avoid such disruption. Therefore, we introduce and evaluate a multi-objective evolutionary approach that minimises disruption while maximising cohesion/coupling improvement. This allows developers to balance reticence to disrupt existing modular structure, against their competing need to improve cohesion and coupling. The multi-objective approach is able to find modular structures that improve the cohesion of developers’ implementations by 22.52%, while causing an acceptably low level of disruption (within that already tolerated by developers)

Molecular Image Segmentation Based on Improved Fuzzy Clustering

Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage is the texture energy characterization using a Gabor wavelet method. The third stage is introducing spatial constraints provided by the denoising data and the textural information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve 0.96 ± 0.03 segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm

Soil Carbon and Nitrogen Stocks and Their Relationship with Plant and Soil Dynamics of Degraded and Artificial Restoration Grasslands in an Alpine Region

Land disturbances and management approaches can significantly alter grassland soils. Therefore, understanding the carbon and nitrogen storage accompanying plant and soil physical and chemical properties due to anthropogenic disturbance and different management strategies is important. In our study, we investigated carbon and nitrogen storage in artificial grasslands with different durations of restoration and native grasslands with different levels of degradation. We found that total carbon and nitrogen were significantly higher five years after restoration than after seven and nine years, but decreased due to grassland degradation. Furthermore, soil carbon and nitrogen had a close relationship with plant and soil factors, as reflected by a correlation index. The above-mentioned results indicate that artificial grasslands can be used as an effective method to restore “black-beach” soil grassland. In the long term, however, human intervention should be implemented to prevent the degradation of artificial grasslands