Do automated program repair techniques repair hard and important bugs?

Existing evaluations of automated repair techniques focus on the fraction of the defects for which the technique can produce a patch, the time needed to produce patches, and how well patches generalize to the intended specification. However, these evaluations have not focused on the applicability of repair techniques and the characteristics of the defects that these techniques can repair. Questions such as “Can automated repair techniques repair defects that are hard for developers to repair?” and “Are automated repair techniques less likely to repair defects that involve loops?” have not, as of yet, been answered. To address such questions, we annotate two large benchmarks totaling 409 C and Java defects in real-world software, ranging from 22K to 2.8M lines of code, with measures of the defect’s importance, the developer-written patch’s complexity, and the quality of the test suite. We then analyze relationships between these measures and the ability to produce patches for the defects of seven automated repair techniques —AE, GenProg, Kali, Nopol, Prophet, SPR, and TrpAutoRepair. We find that automated repair techniques are less likely to produce patches for defects that required developers to write a lot of code or edit many files, or that have many tests relevant to the defect. Java techniques are more likely to produce patches for high-priority defects. Neither the time it took developers to fix a defect nor the test suite’s coverage correlate with the automated repair techniques’ ability to produce patches. Finally, automated repair techniques are less capable of fixing defects that require developers to add loops and new function calls, or to change method signatures. These findings identify strengths and shortcomings of the state-of-the-art of automated program repair along new dimensions. The presented methodology can drive research toward improving the applicability of automated repair techniques to hard and important bugs.     

Influence of birth rates and transmission rates on the global seasonality of rotavirus incidence

Rotavirus is a major cause of mortality in developing countries, and yet the dynamics of rotavirus in such settings are poorly understood. Rotavirus is typically less seasonal in the tropics, although recent observational studies have challenged the universality of this pattern. While numerous studies have examined the association between environmental factors and rotavirus incidence, here we explore the role of intrinsic factors. By fitting a mathematical model of rotavirus transmission dynamics to published age distributions of cases from 15 countries, we obtain estimates of local transmission rates. Model-predicted patterns of seasonal incidence based solely on differences in birth rates and transmission rates are significantly correlated with those observed (Spearman''s ρ = 0.65, p < 0.05). We then examine seasonal patterns of rotavirus predicted across a range of different birth rates and transmission rates and explore how vaccination may impact these patterns. Our results suggest that the relative lack of rotavirus seasonality observed in many tropical countries may be due to the high birth rates and transmission rates typical of developing countries rather than being driven primarily by environmental conditions. While vaccination is expected to decrease the overall burden of disease, it may increase the degree of seasonal variation in the incidence of rotavirus in some settings.     

Powder Compaction Dies and Compressibility of Various Materials

Powder Metallurgy and Metal Ceramics - Powder compaction is the most crucial process in powder metallurgy since almost all the desired properties of a material, such as a shape, size, density,...     

An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

The present study introduces an efficient algorithm for automatic segmentation and detection of mass present in the mammograms. The problem of over and under-segmentation of low-contrast mammographic images has been solved by applying preprocessing on original mammograms. Subtraction operation performed between enhanced and enhanced inverted mammogram significantly highlights the suspicious mass region in mammograms. The segmentation accuracy of suspicious region has been improved by combining wavelet transform and fast fuzzy c-means clustering algorithm. The accuracy of mass segmentation has been quantified by means of Jaccard coefficients. Better sensitivity, specificity, accuracy, and area under the curve (AUC) are observed with support vector machine using radial basis kernel function. The proposed algorithm is validated on Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Highest 91.76% sensitivity, 96.26% specificity, 95.46% accuracy, and 96.29% AUC on DDSM dataset and 94.63% sensitivity, 92.74% specificity, 92.02% accuracy, and 95.33% AUC on MIAS dataset are observed. Also, shape analysis of mass is performed by using moment invariant and Radon transform based features. The best results are obtained with Radon based features and achieved accuracies for round, oval, lobulated, and irregular shape of mass are 100%, 70%, 64%, and 96%, respectively.     

Generalized attitudinal Choquet integral

Attitudinal Choquet integral (ACI) extends Choquet integral (CI) through a consideration of a decision-maker's (DM's) attitudinal character. In this paper, we generalize ACI, and the resulting operator is termed as generalized ACI (GACI). GACI adds to the efficacy of the ACI in the representation of a DM's unique and complex attitudinal character. It also generates a vast range of exponential ACI operators, such as harmonic ACI, ACI, quadratic ACI, to name a few. We further present induced GACI to consider additional information that may be associated with the arguments of aggregation. The special cases of the proposed operators are investigated. The usefulness of the proposed operators in modelling human decision behavior is shown through a case study.     

Attitudinal choice models with applications in human decision making

A new family of attitudinal discrete choice models is proposed by considering the attitudinal character and the weight vector, both of which are specific to a decision maker (DM). Given the attribute values of different alternatives, the proposed models give varying choice probabilities, as per the DM's-specific attitudinal character and the weight vector. It is also shown that the conventional discrete choice models are the special cases of the proposed attitudinal models. The proposed choice models are also generalized through an additional parameter to add to their capabilities. An application on real data is included to demonstrate their usefulness in the real world.     

Soft information set for multicriteria decision making

An information source value is perceived differently by different agents. In this paper, we present a new knowledge representation structure, termed as soft information set (SIS), to provide a parameterized representation of the information values, as perceived by an agent. The properties of SIS are investigated and the notion of relations in SIS is devised. The significance of the proposed SIS in aiding multi criteria decision making is illustrated through real case-studies.     

3D multi-material and multi-joint topology optimization with tooling accessibility constraints

Structural and Multidisciplinary Optimization - This paper proposes a method for performing both multi-material topology optimization and multi-joint topology optimization. The algorithm can...     

IoT Based Multimode Sensing Platform for Underground Coal Mines

Wireless Personal Communications - Real-time information of the underground environment plays a vital role for underground coal mines. For this purpose, a number of systems have been developed by...