The Tricorder methodology uses computational resource monitoring to detect anomalies in low-testability software but faces limitations due to the high data collection time, hindering its practical adoption. This paper proposes a parallel approach to the monitoring phase using an MIMD architecture with distributed memory, implemented with MPI and Python. The algorithm was designed using task partitioning, allowing independent execution across multiple nodes. Experiments showed a significant reduction in monitoring time while maintaining consistency in the collected data. The proposed approach contributes to making the Tricorder methodology more efficient and feasible for large-scale testing scenarios.

The popularization of Bitcoin and other cryptocurrencies has motivated the interest in using blockchain infrastructure in contexts other than the original. This is due to blockchain allows for a distribution of data with decentralized management and in a secure environment. In this scenario, the goal of this work is to evaluate the impact of database usage when blockchain is employed to manipulate large volumes of heterogeneous data. The methodology used in our evaluation considers the Hyperledger Fabric to set up a network for sharing medical data, which is obtained from a real database. The performance of this network was collected through experimental studies, with the Hyperledger Caliper benchmark, by measuring the throughput and latency of the network with and without the CouchDB database. Our results show the impact of the overhead imposed by the database when it is used in a blockchain network. This work contributes to future developers of blockchain applications as it shows the impact of database usage on such applications.

This paper proposes novel techniques for the extraction of structural information from the source code of Java concurrent programs for their coverage testing. Such techniques differ from others because they consider synchronization flow among processes/threads, distinct paradigms of communication/synchronization, method calls and pointer manipulation. The structural information gathered from the source code is kept in a test model based on a Parallel Control Flow Graph (PCFG) and helps the generation of an instrumented source code, used for a future generation of trace files and to replay the concurrent execution. The results show the techniques can generate both an instrumented code and a PCFG for Java concurrent programs effectively, extracting static and runtime information required for structural testing.

In this paper we propose a parallel approach for the K-meansVector Quantization (VQ) algorithm used in a two-stageHidden Markov Model (HMM)-based system forrecognizing handwritten numeral strings. With thisparallel algorithm, based on the master/slave paradigm,we overcome two drawbacks of the sequential version: a)the time taken to create the codebook; and b) the amountof memory necessary to work with large trainingdatabases. Distributing the training samples over theslaves' local disks reduces the overhead associated withthe communication process. In addition, modelspredicting computation and communication time havebeen developed. These models are useful to predict theoptimal number of slaves taking into account the numberof training samples and codebook size.