The Multi-Criteria Test Suite Minimization (MCTSM) problem aims to remove redundant test cases, guided by adequacy criteria such as code coverage or fault detection capability. However, current techniques either exhibit a high loss of fault detection ability or face scalability challenges due to the NP-hard nature of the problem, which limits their practical utility. We propose TripRL , a novel technique that integrates traditional criteria such as statement coverage and fault detection ability with test coverage similarity into an Integer Linear Program (ILP), to produce a diverse reduced test suite with high test effectiveness. TripRL leverages bipartite graph representation and its embedding for concise ILP formulation and combines ILP with effective Reinforcement Learning (RL) training. This combination renders large-scale test suite minimization more scalable and enhances test effectiveness. Our empirical evaluations demonstrate that TripRL ’s runtime scales linearly with the magnitude of the MCTSM problem. Notably, for large test suites from the Defects4J dataset where existing approaches fail to provide solutions within a reasonable time frame, our technique consistently delivers solutions in less than 47 minutes. The reduced test suites produced by TripRL also maintain the original statement coverage and fault detection ability while having a higher potential to detect unknown faults.

In the recent paradigm of cloud radio access networks (C-RAN), signal processing functions at the base stations (BSs) are virtualized and migrated into a mobile cloud that maintains a pool of virtual BS (VBS) instances. Remote radio heads and antennae at the BSs are connected to the VBS pool by fronthaul fiber links. Mobile operators may lease resources from the tower company who owns the C-RAN infrastructure. We study auction mechanisms for efficiently sharing C-RAN resources among mobile operators. Leveraging randomized rounding, we design an offline C-RAN auction mechanism that can achieve truthfulness and near-optimal social welfare. For the more realistic setting of online bid arrival, we design an online algorithm that executes in polynomial time and achieves a competitive ratio of $(1-\epsilon )$ . A tailored fractional Vickrey-Clarke-Groves mechanism works in concert with the online algorithm to elicit truthful bids. Extensive simulation studies verify the efficacy of our C-RAN auction mechanisms.

Network Function Virtualization (NFV) is emerging as a new paradigm for providing elastic network functions through flexible virtual network function (VNF) instances executed on virtualized computing platforms exemplified by cloud datacenters. In the new NFV market, well defined VNF instances each realize an atomic function that can be chained to meet user demands in practice. This work studies the dynamic market mechanism design for the transaction of VNF service chains in the NFV market, to help relinquish the full power of NFV. Combining the techniques of primal-dual approximation algorithm design with Myerson's characterization of truthful mechanisms, we design a VNF chain auction that runs efficiently in polynomial time, guarantees truthfulness, and achieves near-optimal social welfare in the NFV eco-system. Extensive simulation studies verify the efficacy of our auction mechanism.