Localizing a bug in a program can be a complex and timeconsuming process. In this paper we propose a code coveragebased fault localization method to prioritize suspicious code in terms of its likelihood of containing program bugs. Code with a higher risk should be examined before that with a lower risk, as the former is more suspicious (i.e., more likely to contain program bugs) than the latter. We also answer a very important question: How can each additional test case that executes the program successfully help locate program bugs? We propose that with respect to a piece of code, the aid introduced by the first successful test that executes it in computing its likelihood of containing a bug is larger than or equal to that of the second successful test that executes it, which is larger than or equal to that of the third successful test that executes it, etc. A tool, χDebug, was implemented to automate the computation of the risk of the code and the subsequent prioritization of suspicious code for locating program bugs. A case study using the Siemens suite was also conducted. Data collected from our study support the proposal described above. They also indicate that our method (in particular Heuristics III (c), (d), and (e)) can effectively reduce the search domain for locating program bugs.

Fault localization is the most expensive activity in program debugging. Traditional ad-hoc methods can be time-consuming and ineffective because they rely on programmers' intuitive guesswork, which may neither be accurate nor reliable. A better solution is to utilize a systematic and statistically well-defined method to automatically identify suspicious code that should be examined for possible fault locations. We present a crosstab-based statistical method using the coverage information of each executable statement and the execution result (success or failure) with respect to each test case. A crosstab is constructed for each executable statement and a statistic is computed to determine the suspiciousness of the corresponding statement. Statements with a higher suspiciousness are more likely to contain bugs and should be examined before those with a lower suspiciousness. Three case studies using the Siemens suite, the Space program, and the Unix suite, respectively, are conducted. Our results suggest that the crosstab-based method is effective in fault localization and performs better (in terms of a smaller percentage of executable statements that have to be examined until the first statement containing the fault is reached) than other methods such as Tarantula. The difference in efficiency (computational time) between these two methods is very small.

Hybrid fuzzing which combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, the state-of-theart hybrid fuzzing systems deploy "demand launch" and "optimal switch" strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to oversimplified assumptions. We then propose a novel "discriminative dispatch" strategy to better utilize the capability of concolic execution. We design a Monte Carlo based probabilistic path prioritization model to quantify each path's difficulty and prioritize them for concolic execution. This model treats fuzzing as a random sampling process. It calculates each path's probability based on the sampling information. Finally, our model prioritizes and assigns the most difficult paths to concolic execution. We implement a prototype system DigFuzz and evaluate our system with two representative datasets. Results show that the concolic execution in DigFuzz outperforms than those in state-of-the-art hybrid fuzzing systems in every major aspect. In particular, the concolic execution in DigFuzz contributes to discovering more vulnerabilities (12 vs. 5) and producing more code coverage (18.9% vs. 3.8%) on the CQE dataset than the concolic execution in Driller.

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Abstract-Heterogeneous feature representations are widely used in machine learning and pattern recognition, especially for multimedia analysis. The multi-modal, often also highdimensional, features may contain redundant and irrelevant information that can deteriorate the performance of modeling in classification. It is a challenging problem to select the informative features for a given task from the redundant and heterogeneous feature groups. In this paper, we propose a novel framework to address this problem. This framework is composed of two modules, namely, multi-modal deep neural networks and feature selection with sparse group LASSO. Given diverse groups of discriminative features, the proposed technique first converts the multi-modal data into a unified representation with different branches of the multi-modal deep neural networks. Then, through solving a sparse group LASSO problem, the feature selection component is used to derive a weight vector to indicate the importance of the feature groups. Finally, the feature groups with large weights are considered more relevant and hence are selected. We evaluate our framework on three image classification datasets. Experimental results show that the proposed approach is effective in selecting the relevant feature groups and achieves competitive classification performance as compared with several recent baseline methods.

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Intermediate‐temperature proton ceramic fuel cells (PCFCs)–a promising power generation technology–have attracted significant attention in recent years because of their unique advantages over conventional high‐temperature solid oxide fuel cells and low‐temperature proton exchange membrane fuel cells. The cathodes of PCFCs simultaneously require efficient channels for proton, oxide‐ion, and electron transfer; therefore, designing and engineering cathode materials with tailorable H+, O2−, and e− conductivities are crucial for improving PCFC performance. Despite significant efforts and critical progress in this field, exploring the desired cathode materials remains challenging. This review provides a comprehensive and critical overview of oxide materials for PCFC cathodes, particularly triple H+/O2−/e− conductors. Their proton uptake, conduction mechanisms, and structure–property relationships are focused on to guide future material design. In addition, the electrochemical performance of these cathode materials in PCFCs is discussed and the electrochemical performance gaps among PCFCs with different types of cathode materials are defined. Finally, perspectives on the development of high‐performance PCFCs are proposed.