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In this paper, we present CALSAGOS : Clustering ALgorithmS Applied to Galaxies in Overdense Systems which is a PYTHON package developed to select cluster members and to search, find, and identify substructures. CALSAGOS is based on clustering algorithms, and was developed to be used in spectroscopic and photometric samples. To test the performance of CALSAGOS , we use the S-PLUS's mock catalogues, and we found an error of 1–6 per cent on member selection depending on the function that is used. Besides, CALSAGOS has a F 1-score of 0.8, a precision of 85 per cent and a completeness of 100 per cent in the identification of substructures in the outer regions of galaxy clusters (r > r 200). The F 1-score, precision, and completeness of CALSAGOS fall to 0.5, 75, and 40 per cent when we consider all substructure identifications (inner and outer) due to the function that searches, finds, and identifies the substructures works in 2D, and cannot resolve the substructures projected over others. [ABSTRACT FROM AUTHOR]

During component-based and object-oriented software development, software classes exhibit relationships that complicate integration, including method calls, inheritance and aggregation. Classes are integrated and tested in specific orders, where each class is added and tested one by one to see if it integrates successfully. A difficulty arises when cyclic dependencies exist—the functionality that is used by the first class to be tested must be mimicked by creating ‘stubs’ (sometimes called ‘mock objects’), an expensive and error-prone operation. This problem is generally called the class integration and test order (CITO) problem, and solutions must fully be automated for integration and testing to proceed smoothly and efficiently. This paper describes new techniques and algorithms to solve the CITO problem. New results include improved edge weights to more precisely model the cost of stubbing, and the use of node weights, which allows more information to be used. These weights are derived from quantitative measures of couplings between the integrated and the stubbed classes. Also, a new algorithm for computing the integration and test orders is presented. The technique is compared with an existing approach and found to be cheaper, get the same results when using edge weights exclusively, and yield better results when using node weights. [ABSTRACT FROM AUTHOR]