01700nas a2200217   4500008004100000245009300041210006900134260000900203300001300212490000600225520098800231653002901219653001801248653002101266653002001287653002101307100001101328700001401339700001201353856011701365       2007                        eng d00aEmpirically validating software metrics for risk prediction based on intelligent methods0 aEmpirically validating software metrics for risk prediction base  c2007  a99 - 1060 v53 aThe software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.10aBayesian belief networks10aDecision tree10aNearest neighbor10aRisk prediction10aSoftware metrics1 aXu, Z.1 aZheng, X.1 aGuo, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350695759&partnerID=40&md5=f939c6d69c641cbeee0b6e64381ba65a01655nas a2200205   4500008004100000245010900041210006900150260000900219300001400228490000600242520095500248653002301203653002701226653002101253653002101274100001401295700001201309700001101321856011701332       2007                        eng d00aKnowledge-based support for object-oriented software design and synthesis: A category theoretic approach0 aKnowledgebased support for objectoriented software design and sy  c2007  a115 - 1220 v53 aTo reuse previous knowledge of objectoriented design and adapt them to solve new problems, the collaboration relationships and the responsibility distribution among software objects need to be thoroughly understood and precisely formulated. The paper proposes a knowledge-based approach that employs category theoretic models to formalize and mechanize objectoriented software design and synthesis by focusing concern on reasoning about the interdependency relationships at different levels of abstraction and granularity. The major benefit of our approach is twofold: First, it provides an explicit semantics for formal object-oriented specifications, and therefore enables a high-level of reusability and dynamic adaptability. Second, it utilizes the ability of categorical computations to support automated software composition and refinement. A prototype tool that demonstrates the feasibility and effectiveness of our approach is also presented.10aAutomated software10aObject oriented design10aSoftware metrics10aSoftware quality1 aZheng, Y.1 aXue, J.1 aHu, Q.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-70350673897&partnerID=40&md5=de1990a30fe9efe685a0df6e39e37b82