Unique-Cause MC/DC (UCM) is the most desired form of MC/DC in many safety-critical applications. For a given predicate, the UCM considers the independent pair of each condition by flipping the corresponding condition and fixing the other conditions. For the given N conditions in a predicate, the existing static symbolic execution tool, CBMC, generates MC/DC (Modified Condition/Decision) goal constraints of size, N + 1 that constitutes its minimal independent pairs. However, we propose a novel UCM Sequence Generator (UCM-Gen) that generates all possible inequality comparisons of the sequences/combinations of the N conditions which helps in computing the independent pairs further. The UCM-Gen outputs UCM Annotated Program which when given to the program verifiers, produces the UCM Score (%). In our work, we have considered CBMC to get the SAT/UNSAT results for each sequence of the UCM Annotated Program. Upon analysing these results, we calculate the total number of independently affected conditions (i.e., I value) for all the predicates in the given program. Furthermore, this work is compared with the CBMC's mode of MC/DC implementation. Interestingly, our proposed approach based UCM score (%) is always greater than the CBMC's MC/DC score (%) and hence claiming that their corresponding test cases contribute in effective bug finding.

This article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Economic dispatch in a multi-microgrid (MMG) system involves an increasing number of states from distributed energy resources (DERs) compared to a single microgrid. In these cases, traditional reinforcement learning (RL) approaches may become computationally expensive or less effective in finding the least-cost solution. This paper presents a novel RL approach that employs local learning agents to interact with individual microgrid environments in a distributed manner and a global agent to search for actions to minimize system cost at the MMG system level. The proposed distributed RL framework is more efficient in learning the dispatch policy compared to conventional approaches. Case studies are performed on a 3-microgrid system with different types of DERs. Results substantiate the effectiveness of the proposed approach in comparison with conventional methods in terms of operation costs, computation time, and peak-to-average ratio.

Distributed concolic testing (DCT) for complex programs takes a remarkable computational time. Also, the achieved modified condition/decision coverage (MC/DC) for such programs is often inadequate. We propose an improved DCT approach that reduces the computational time and simultaneously enhanced the MC/DC. We have named our approach SMCDCT (scalable MC/DC percentage calculator using DCT). Our experimental study on forty-five C programs indicates 6.62% of average increase in MC/DC coverage. Copyright © 2016 John Wiley & Sons, Ltd.