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Automated program repair is an effective way to ensure software quality and improve software development efficiency. At present, there are many methods and tools of automated program reapir in real world, but most of them have low repair accuracy, resulting in a large number of incorrect patches in the generated patches. To solve this problem, we propose a patch quality evaluation method based on semantic distance, which measures the semantic distance of patches by using features of interval distance, output coverage, and path matching. For each evaluation feature, we give a quantitative formula to obtain a specific distance value and use the distance to calculate the recommended patch value to measure the quality of the patch. Our quality evaluation method evaluated 279 patches from previous program repair tools, including Nopol, DynaMoth, ACS, jGenProg, and CapGen. This quality evaluation method successfully arranged the correct patches before the plausible but incorrect patches, and it recommended the higher-ranked patches to users first. On this basis, we compared our evaluation method with the existing evaluation methods and judged the evaluation ability of each feature. We showed that our proposed patch quality evaluation method can improve the repair accuracy of repair tools.

Automated program repair is an effective way to ensure software quality and improve software development efficiency. At present, there are many methods and tools of automated program reapir in real world, but most of them have low repair accuracy, resulting in a large number of incorrect patches in the generated patches. To solve this problem, we propose a patch quality evaluation method based on semantic distance, which measures the semantic distance of patches by using features of interval distance, output coverage, and path matching. For each evaluation feature, we give a quantitative formula to obtain a specific distance value and use the distance to calculate the recommended patch value to measure the quality of the patch. Our quality evaluation method evaluated 279 patches from previous program repair tools, including Nopol, DynaMoth, ACS, jGenProg, and CapGen. This quality evaluation method successfully arranged the correct patches before the plausible but incorrect patches, and it recommended the higher-ranked patches to users first. On this basis, we compared our evaluation method with the existing evaluation methods and judged the evaluation ability of each feature. We showed that our proposed patch quality evaluation method can improve the repair accuracy of repair tools. [ABSTRACT FROM AUTHOR]

In recent years, test-based automatic program repair has attracted widespread attention. However, the test suites in practice are not perfect ways to guarantee the correctness of patches generated by repair tools, and weak test suites lead to a large number of incorrect patches produced by the existing repair tool. To reduce the number of incorrect patches generated by repair tools, we propose a patch quality evaluation method based on syntax distance metrics, which measures the syntax distance of patches through four evaluation features—variable, expression, structure, and repair location. By fusing the distance values of the four features, the quality of a patch can be evaluated. Our method evaluates 368 patches from multiple famous repair tools, such as jKali, Nopol, SimFix, DynaMoth, and CapGen; 95% of the correct patches were ranked in the top one of the plausible patches for each defect, which indicates our method can find high-quality patches. [ABSTRACT FROM AUTHOR]

In recent years, test-based automatic program repair has attracted widespread attention. However, the test suites in practice are not perfect ways to guarantee the correctness of patches generated by repair tools, and weak test suites lead to a large number of incorrect patches produced by the existing repair tool. To reduce the number of incorrect patches generated by repair tools, we propose a patch quality evaluation method based on syntax distance metrics, which measures the syntax distance of patches through four evaluation features—variable, expression, structure, and repair location. By fusing the distance values of the four features, the quality of a patch can be evaluated. Our method evaluates 368 patches from multiple famous repair tools, such as jKali, Nopol, SimFix, DynaMoth, and CapGen; 95% of the correct patches were ranked in the top one of the plausible patches for each defect, which indicates our method can find high-quality patches.

In order to improve the quality of fingerprint with a large noise, this study proposes a fingerprint enhancement method by using a sparse representation of learned multi‐scale classification dictionaries with reduced dimensionality. The multi‐scale dictionary is used to balance the contradiction between the accuracy and the anti‐noise ability, which is an ideal solution to reconcile the demands of enhancement quality and computational performance. The principal component analysis is applied in the authors' technique for dimension reduction of multi‐scale classification dictionaries. Under the quality grading scheme and multi‐scale composite windows, the fingerprint patches are enhanced by using a sparse representation of learned multi‐scale classification dictionaries with reduced dimensionality according to their priorities. In addition, the multi‐scale composite windows help the more high‐quality spectra diffuse into the low‐quality fingerprint patches and this can greatly improve the spectra quality of them. Experimental results and comparisons on FVC 2000 and FVC 2004 databases are reported. And it shows that the proposed method yields better results in terms of the robustness of fingerprint enhancement as compared with the latest techniques. Moreover, the results show that the proposed algorithm can obtain better identification performance. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the present work is to mathematically model the reliability growth of a multi-version software system that is affected by infected patches. Design/methodology/approach: The work presents a mathematical model that studies the reliability change due to the insertion of an infected patch in multi-version software. Various distribution functions have been considered to highlight the varied aspects of the model. Furthermore, weighted criteria approach has been discussed to facilitate the choice of the model. Findings: The model presented here is able to quantify the effect of an infected patch on multi-version software. The model captures the hike in bug content due to an infected patch. Originality/value: Multi-version systems have been studied widely, but the role of an infected patch has not been yet explored. The effect of an infected patch has been quantified by modeling the extra bugs generated in the system. This bug count would prove helpful in further studies for optimal resource allocation and testing effort allocation. [ABSTRACT FROM AUTHOR]

Populations of European freshwater pearl mussels, Margartitfera margaritifera, have declined dramatically throughout their range. Better knowledge of postlarval feeding is required to determine factors influencing juvenile survival and growth, both in captivity and in natural streams. Development of the filtering organ represents a critical stage in juveniles. The filtering organ (ctenidium) in postlarvae (shell length, L = 1-3 mm) was studied by quantitative histology. Ctenidial development depended on mussel size, not age and was not accelerated by exposing the postlarvae to a 100-d artificial winter. Three distinct ctenidial stages were identified: the I-, V- and W-stages, which describe the ctenidial shapes, starting at L = 0.8, 1.1 and 4.5 mm, respectively. Functional redundancy of pedal and filter feeding probably reduces winter mortality at I-stage and early V-stage. Filter feeding becomes efficient at c. L = 2.2 mm, when the filtration area reaches 2.1 mm2, inducing an ontogenetic shift from pedal feeding to ctenidial filter feeding. For every 123 µm increase in L, one additional filament develops posteriorly from a budding zone. Filament thickness (c. 28 µm) and the distance between them (c. 29 µm) do not change with mussel size, indicating that juveniles and adults capture the same food particles. The change from pedal feeding to filter feeding represents an ontogenetic feeding shift and hence represents a critical stage in survival of juvenile M. margaritifera. [ABSTRACT FROM AUTHOR]

Many plant species employing a food-deceptive pollination strategy show discrete or continuous floral polymorphism within their populations. Previous studies have suggested that negative frequencydependent selection (NFDS) caused by the learning behavior of pollinators was responsible for the maintenance of floral polymorphism. However, NFDS alone does not explain why and when discrete or continuous polymorphism evolves. In this study, we use an evolutionary simulation model to propose that inaccurate discrimination of flower colors by pollinators results in evolution of discrete flower color polymorphism. Simulations showed that associative learning based on inaccurate discrimination in pollinators caused disruptive selection of flower colors. The degree of inaccuracy determined the number of discrete flower colors that evolved. Our results suggest that animal behavior based on inaccurate discriminationmay be a general cause of disruptive selection that promotes discrete trait polymorphism. [ABSTRACT FROM AUTHOR]