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Spectrum-based fault localization (SFL) techniques have shown considerable effectiveness in localizing software faults. They leverage a ranking metric to automatically assign suspiciousness scores to certain entities in a given faulty program. However, for some programs, the current SFL ranking metrics lose effectiveness. In this paper, we introduce ConsilientSFL that is served to synthesize a new ranking metric for a given program, based on a customized combination of a set of given ranking metrics. ConsilientSFL can be significant since it demonstrates the usage of voting systems into a software engineering task. First, several mutated, faulty versions are generated for a program. Then, the mutated versions are executed with the test data. Next, the effectiveness of each existing ranking metric is computed for each mutated version. After that, for each mutated version, the computed existing metrics are ranked using a preferential voting system. Consequently, several top metrics are chosen based on their ranks across all mutated versions. Finally, the chosen ranking metrics are normalized and synthesized, yielding a new ranking metric. To evaluate ConsilientSFL, we have conducted experiments on 27 subject programs from Code4Bench and Siemens benchmarks. In the experiments, we found that ConsilientSFL outperformed every single ranking metric. In particular, for all programs on average, we have found performance measures recall, precision, f-measure, and percentage of code inspection, to be nearly 7, 9, 12, and 5 percentages larger than using single metrics, respectively. The impact of this work is twofold. First, it can mitigate the issue with the choice and usage of a proper ranking metric for the faulty program at hand. Second, it can help debuggers find more faults with less time and effort, yielding higher quality software. [ABSTRACT FROM AUTHOR]

In a critical environment, e.g. in a factory where an employee faces hazardous conditions, monitoring the health status of the employee is important. Thus, continuous connectivity of the employee to the network is the main concern of such networks. In this paper, we have proposed a decentralized approach for mobility management of mobile nodes in hazardous areas like factory. The proposed mobility structure for hazardous areas, called MSHA, organizes static nodes as a tree for an efficient routing, automatic addressing, and handling movement of mobile nodes. MSHA is capable of handling multiple failures of static nodes which disconnect a mobile node from the network. MSHA is highly scalable regarding the number of mobile nodes and the size of the covered monitoring area. The proposed scheme is evaluated based on different factors. The results reveal the superiority of MSHA compared with the previous works. The promising analytical results manifest the performance (about 20%) of MSHA specifically in reducing packet loss and hand-off delay caused by the failure of the static nodes. The performance does not degrade with increasing the number of mobile nodes. [ABSTRACT FROM AUTHOR]

The IOT infrastructure - IP-based mobile sensor network- makes it possible to provide two-directional communication between mobile sensors and the remote server. Mobility direction prediction is a major challenge in IP-based networks by which we can predict the next movement direction of moving objects carrying mobile node(s). In this paper, we have introduced a Distributed Self-Healing Movement Prediction scheme for IOT applications, so-called DSHMP-IOT, to predict movement direction of mobile IP-based sensors in a multi-user environment, such as a health-care system. This is the first time that an AI solution is applied to predict the direction of the mobile node(s) in an IP-based mobile network. The proposed scheme takes advantage of Hidden Semi-Markov Model (HSMM) to predict the movement direction with high accuracy and low overhead. The previous works for estimating the direction of a mobile node(s) in IP-based mobile networks are based on AOA, a hardware-specific method. The proposed scheme has several advantages. First, it eliminates the need for special hardware (directional antenna, an antenna array, etc.) which is required in AOA based methods. Second, it is not sensitive to noise, speed and sudden changing of movement direction which cause false positive movement direction prediction in AOA method. Third, in this context, it is the only work with self-healing capability whenever one or more static sensors fail(s). Fourth, it includes a recovery mechanism which prevents the mobile node from being disconnected in case of false prediction of our learning model. The simulation results show the superiority of our scheme regarding power consumption and hand-off delay, as well as packet loss, compared to similar approaches. [ABSTRACT FROM AUTHOR]

Considering the fact that faults may be revealed as undesired mutual effect of program predicates on each other, a new approach for localizing latent bugs, namely Hierarchy-Debug, is presented in this paper. To analyze the vertical effect of predicates on each other and on program termination status, the predicates are fitted into a logistic lasso model. To support scalability, a hierarchical clustering algorithm is applied to cluster the predicates according to their presence in different executions. Considering each cluster as a pseudo-predicate, a distinct lasso model is built for intermediate levels of the hierarchy. Then, we apply a majority voting technique to score the predicates according to their lasso coefficients at different levels of the hierarchy. The predicates with relatively higher scores are ranked as fault relevant predicates. To provide the context of failure, faulty sub-paths are identified as sequences of fault relevant predicates. The grouping effect of Hierarchy-Debug helps programmers to detect multiple bugs. Four case studies have been designed to evaluate the proposed approach on three well-known test suites, Space, Siemens, and Bash. The evaluations show that Hierarchy-Debug produces more precise results compared with prior fault localization techniques on the subject programs. [ABSTRACT FROM AUTHOR]