Software developers can only obtain a very small amount of information from the individual failure-causing inputs, which makes debugging difficult. Therefore, it is necessary to explore additional failure-causing inputs (failure regions) using the known failure-causing inputs. In order to accurately and efficiently identify the failure region, we propose a novel two-stage search algorithm, TS-FRI. In the initial exploration stage, a round-robin search identifies several boundary failure-causing points, and the failure region's centroid is estimated. During the main search stage, the boundary failure-causing points are identified through iterative division of the input domain with an equally sized partitioning strategy. This results in the boundary points being as dispersed as possible around the failure-region boundary, with the polytope formed by the points approximating the failure region (e.g., a polygon in two dimensions). The proposed algorithm is validated through simulation and empirical analysis: The experimental results show that the TS-FRI accuracy is at least comparable to the best accuracy of the compared three algorithms, and can be ten times better. In addition, TS-FRI only takes a quarter of the computation time and half the failure-validation cost of the other algorithms.

Summary When selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability.

Diversity is the key factor for test cases to detect program failures. Adaptive random testing (ART) is one of the effective methods to improve the diversity of test cases. Being an ART algorithm, the evolutionary adaptive random testing (eAR) only increases the distance between test cases to enhance its failure detection ability. This paper presents a new ART algorithm, MoesART, based on multi-objective evolutionary search. In this algorithm, in addition to the dispersion diversity, two other new diversities (or optimization objectives) are designed from the perspectives of the balance and proportionality of test cases. Then, the Pareto optimal solution returned by the NSGA-II framework is used as the next test case. In the experiments, the typical block failure pattern in the cases of two-dimensional and three-dimensional input domains is used to validate the effectiveness of the proposed MoesART algorithm. The experimental results show that MoesART exhibits better failure detection ability than both eAR and the fixed-sized-candidate-set ART (FSCS-ART), especially for the programs with three-dimensional input domain.