Test case prioritization (TCP) has been widely studied in regression testing, which aims to optimize the execution order of test cases so as to detect more faults earlier. TCP has been divided into white-box test case prioritization (WTCP) and black-box test case prioritization (BTCP) . WTCP can achieve better prioritization effectiveness by utilizing source code information, but is not applicable in many practical scenarios (where source code is unavailable, e.g., outsourced testing). BTCP has the benefit of not relying on source code information, but tends to be less effective than WTCP. That is, both WTCP and BTCP suffer from limitations in the practical use. To improve the practicability of TCP, we aim to explore better BTCP, significantly bridging the effectiveness gap between BTCP and WTCP. In this work, instead of statically analyzing test cases themselves in existing BTCP techniques, we conduct the first study to explore whether this goal can be achieved via log analysis. Specifically, we propose to mine test logs produced during test execution to more sufficiently reflect test behaviors, and design a new BTCP framework (called LogTCP), including log pre-processing, log representation, and test case prioritization components. Based on the LogTCP framework, we instantiate seven log-based BTCP techniques by combining different log representation strategies with different prioritization strategies. We conduct an empirical study to explore the effectiveness of LogTCP. Based on 10 diverse open-source Java projects from GitHub, we compared LogTCP with three representative BTCP techniques and four representative WTCP techniques. Our results show that all of our LogTCP techniques largely perform better than all the BTCP techniques in average fault detection, to the extent that they become competitive to the WTCP techniques. That demonstrates the great potential of logs in practical TCP.

In the fields of technology such as aerospace, anomaly detection is critical to the overall system. With the large increase in data volume and dimensions, the traditional detection methods have great limitations, and thus anomaly detection algorithms based on deep learning have received widespread attention. In this paper, based on autoencoder: standard autoencoder, denoising autoencoder, and sparse autoencoder, an ensemble detection model that can extract more feature information is proposed. To make more use of these feature information, inspired by the idea of pooling layer of the CNN, two feature fusion methods are proposed. Finally, the experiment verifies that the result of this model is better than the single autoencoder model.

Life cycle management is an effective way to maintain software quality in safety-critical systems; however, it faces difficulties when applied on software of Programmable Logic Device (PLD). This is because: (1) traditional models on software development do not fit with design workflow of PLD; (2) adaptive models for PLD focus mainly on specific features during software implementation, ignoring high-level design and inspection; (3) no consideration is paid on developing and testing the PLD software based on a reference model (e.g. C/C++ model), which is often used for prototyping complex system, such as spacecraft. In this paper, we propose a novel C-W Model, aiming to complete and normalize the life cycle of software development and testing in programmable logic. We use the C-W Model in different kinds of projects to do debugging in Hardware Description Language (HDL). Experimental results suggest that testing based on the proposed model has high error detection rate and is more efficient compared with the traditional method.