Selecting fault revealing mutants

Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)

Got Issues? Who Cares About It? A Large Scale Investigation of Issue Trackers from GitHub

International audienceFeedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering such questions by performing an empirical study on a hundred thousands of open source projects. After filtering relevant trackers, the study used about 20,000 projects. We investigate and answer various research questions on the popularity and impact of issue trackers

mRNA-Sequencing Analysis Reveals Transcriptional Changes in Root of Maize Seedlings Treated with Two Increasing Concentrations of a New Biostimulant

Biostimulants are a wide range of natural or synthetic products containing substances and/or microorganisms that can stimulate plant processes to improve nutrient uptake, nutrient efficiency, tolerance to abiotic stress, and crop quality ( http://www.biostimulants.eu/ , accessed September 27, 2017). The use of biostimulants is proposed as an advanced solution to face the demand for sustainable agriculture by ensuring optimal crop performances and better resilience to environment changes. The proposed approach is to predict and characterize the function of natural compounds as biostimulants. In this research, plant growth assessments and transcriptomic approaches are combined to investigate and understand the specific mode(s) of action of APR, a new product provided by the ILSA group (Arzignano, Vicenza). Maize seedlings (B73) were kept in a climatic chamber and grown in a solid medium to test the effects of two different combinations of the protein hydrolysate APR (A1 and A1/2). Data on root growth evidenced a significant enhancement of the dry weight of both roots and root/shoot ratio in response to APR. Transcriptomic profiles of lateral roots of maize seedlings treated with two increasing concentrations of APR were studied by mRNA-sequencing analysis (RNA-seq). Pairwise comparisons of the RNA-seq data identified a total of 1006 differentially expressed genes between treated and control plants. The two APR concentrations were demonstrated to affect the expression of genes involved in both common and specific pathways. On the basis of the putative function of the isolated differentially expressed genes, APR has been proposed to enhance plant response to adverse environmental conditions

Selecting fault revealing mutants

Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)

Understanding Android App Piggybacking:A Systematic Study of Malicious Code Grafting

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has produced approaches and tools to identify piggybacked apps, the literature lacks a comprehensive investigation into such phenomenon. We fill this gap by 1) systematically building a large set of piggybacked and benign apps pairs, which we release to the community, 2) empirically studying the characteristics of malicious piggybacked apps in comparison with their benign counterparts, and 3) providing insights on piggybacking processes. Among several findings providing insights, analysis techniques should build upon to improve the overall detection and classification accuracy of piggybacked apps, we show that piggybacking operations not only concern app code but also extensively manipulates app resource files, largely contradicting common beliefs. We also find that piggybacking is done with little sophistication, in many cases automatically, and often via library code

Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification ac- curacy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features

Capabilities of Global Ocean Programmes to Inform Climate Services

AbstractClimate services are identified as a means of providing the information that is needed to support decision makers in assessing the impacts of climate change on the oceans. We discuss the current observation programs to support these services, and their capacity to provide the information needed to monitor and address key science questions. An analysis of the current oceanographic observation programs is shown to be undersubscribed from their original plans. There are vulnerabilities in the current observing programs, particularly in relation to satellite measurements. The interaction of climate services with the research community, with policy makers and stakeholders and operational centres is outlined and leads to four recommendations. The key recommendations are for the more pervasisve development of climate services and for a modest increment in the observing program informed by the recommendations of the OceanObs’09 conference