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Complexity is an important attribute to determine the software quality. Software complexity can be measured during the design phase before implementation of system. At the design phase, UML class diagram is the important diagram to show the relationships among the classes of objects in the system. In this paper, we measure the complexity of object-oriented software at design phase to predict the fault-prone classes. The ability to predict the fault-prone classes can provide guidance for software testing and improve the effectiveness of development process. We constructed the Naive Bayesian and k-Nearest Neighbors model to find the relationship between the design complexity and fault-proneness. The proposed models are empirically evaluated using four version of JEdit. The models had been validated using 10-fold cross validation. The performance of prediction models were evaluated by goodnessof-fit criteria and Receiver Operating Characteristic (ROC) analysis. Results obtained from our case study showed the average of models developed by design complexity can predict up to 70% fault-prone classes in object oriented software. It is a better an early indicator of software quality.

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Content-based image retrieval is an image search techniques from large image database by analyzing features of the image. Image feature can be color, texture, shape, and others. This study uses color and texture features when searching image. Color histogram is used to extract color features with quantization approach to HSV. Texture features in image obtained from the calculation of Gray-Level Co-occurrence Matrix (GLCM) and multi-scale GLCM. Multi-scale GLCM using Gaussian smoothing to reduce noise in the image and considering multiple scale from an image. Image search results obtained from the comparison of the features of color and texture in database using Euclidean distance. The results show an image search on Wang database using color histogram and multi-scale GLCM obtain higher precision value than just taking one of the method or combinations of color histogram and GLCM.