As software systems become larger and more complex, automated software engineering tools play a crucial role for effective software development management, which is a key factor to lead quality software systems. In this work, we present TRICA, an open source-based software development infrastructure. The name of TRICA represents its features such as Traceability, Relationship, Informativeness, Cost-effectiveness, and Automation. Essentially, in TRICA, a continuous integration tool is coupled with a software configuration management tool and an issue tracking tool. We provisioned a mechanism to connect the open source tools in TRICA so that project members use the collaborated information to solve various issues and implementation problems efficiently, and easily share forthcoming issues during the course of the project. We show that TRICA can help to decentralize risks throughout the software development cycle and achieve successful software development.

We propose six new heuristics to find a source-to-destination path that satisfies two or more additive constraints on edge weights. Five of these heuristics become /spl epsi/-approximation algorithms when their parameters are appropriately set. The performance of our new heuristics is compared experimentally with that of two recently proposed heuristics for the same problem.

Computing constrained shortest paths is fundamental to some important network functions such as QoS routing, which is to find the cheapest path that satisfies certain constraints. In particular, finding the cheapest delay-constrained path is critical for real-time data flows such as voice calls. Because it is NP-complete, there has been much research into designing heuristic algorithms that solve the /spl epsiv/-approximation of the problem with an adjustable accuracy. A common approach is to discretize (i.e., scale and round) the link delay or link cost, which transforms the original problem to a simpler one solvable in polynomial time. The efficiency of the algorithms directly relates to the magnitude of the errors introduced during discretization. We propose two techniques that reduce the discretization errors, allowing faster algorithms to be designed. Reducing the overhead of the costly computation for constrained shortest paths is practically important for the design of a high-throughput QoS router, which is limited by both processing power and memory space. Our simulations show that the new algorithms reduce the execution time by an order of magnitude on power-law topologies with 1000 nodes. The reduction in memory space is similar. When there are multiple constraints, the improvement is more dramatic.