196,203 research outputs found
Transport theory of phase space zonal structures
No full textWe adopt gyrokinetic theory to extract the phase space zonal structure from the flux surface averaged particle response, that is, the nonlinear response that is undamped by collisionless processes. We argue that phase space zonal structures are a proper definition for the nonlinear distortion of the plasma reference state and, thus, of the generally non-Maxwellian neighboring nonlinear equilibria consistent with toroidal symmetry breaking fluctuations. Evolution equations for phase space zonal structures are derived and discussed, along with the corresponding density and energy transport equations. It is shown that this approach is consistent with the usual evolution of macroscopic plasma profiles under the action of fluctuation induced fluxes, when the deviation of the reference state from local Maxwellian response is small. In particular, the present results recover those of a previous article [M. V. Falessi and F. Zonca, Phys. Plasmas 25, 032306 (2018)], where transport equations holding on the reference state length scale have been derived using the moment approach introduced in the classical review work by Hinton and Hazeltine
Achieving and Maintaining CMMI Maturity Level 5 in a Small Organization
No full textCMMI (Capability Maturity Model Integration) models are collections of best practices that help organizations improve their processes. This article reports on the authors' experience in achieving and maintaining CMMI Maturity Level 5 in a small organization. Economic achievements, success factors, and lessons learned are reported by using real-life examples from almost 10 years of improvement process. This article could be a valuable and unique reference for practitioners intending to pursue high-maturity CMMI level, particularly in small organization settings. The importance of this topic and lack of similar experience reports make it a valuable contribution to the state of the practice. The first Web extra at http://youtu.be/HMbgNSFxkpE is an audio recording in which IEEE Software Multimedia Editor Davide Falessi speaks with Shane Oleson and Shannon Taylor of Keymind about how the organization achieved and maintained CMMI Maturity Level 5. The second Web extra at http://youtu.be/RKpKBo7roZI is an audio recording in which author Kathy Mullen introduces a custom Web-based tool called the Keymind Measurement Reporting Tool
Technical Panel
No full textModerator : J.-O. PIEDNOIR, Technical Panel : J.-P. MASCARELLI, G. FALESSI, P. BRICAUD, M. VODOVAR, G . JACQUEMODInternational audienc
VALIDATE: a deep dive into vulnerability prediction datasets
No full textContext: Vulnerabilities are an essential issue today, as they cause economic damage to the industry and endanger our daily life by threatening critical national security infrastructures. Vulnerability prediction supports software engineers in preventing the use of vulnerabilities by malicious attackers, thus improving the security and reliability of software. Datasets are vital to vulnerability prediction studies, as machine learning models require a dataset. Dataset creation is time-consuming, error-prone, and difficult to validate. Objectives: This study aims to characterise the datasets of prediction studies in terms of availability and features. Moreover, to support researchers in finding and sharing datasets, we provide the first VulnerAbiLty predIction DatAseT rEpository (VALIDATE). Methods: We perform a systematic literature review of the datasets of vulnerability prediction studies. Results: Our results show that out of 50 primary studies, only 22 studies (i.e., 38%) provide a reachable dataset. Of these 22 studies, only one study provides a dataset in a stable repository. Conclusions: Our repository of 31 datasets, 22 reachable plus nine datasets provided by authors via email, supports researchers in finding datasets of interest, hence avoiding reinventing the wheel; this translates into less effort, more reliability, and more reproducibility in dataset creation and use
Gyrokinetic theory for particle and energy transport in fusion plasmas
No full textA set of equations is derived describing the macroscopic transport of particles and energy in a thermonuclear plasma on the energy confinement time. The equations thus derived allow studying collisional and turbulent transport self-consistently, retaining the effect of magnetic field geometry without postulating any scale separation between the reference state and fluctuations. Previously, assuming scale separation, transport equations have been derived from kinetic equations by means of multiple-scale perturbation analysis and spatio-temporal averaging. In this work, the evolution equations for the moments of the distribution function are obtained following the standard approach; meanwhile, gyrokinetic theory has been used to explicitly express the fluctuation induced fluxes. In this way, equations for the transport of particles and energy up to the transport time scale can be derived using standard first order gyrokinetics. © 2018 EURATOM
Four commentaries on the use of students and professionals in empirical software engineering experiments
No full textThe relative pros and cons of using students or practitioners in experiments in empirical software engineering have been discussed for a long time and continue to be an important topic. Following the recent publication of “Empirical software engineering experts on the use of students and professionals in experiments” by Falessi, Juristo, Wohlin, Turhan, Münch, Jedlitschka, and Oivo (EMSE, February 2018) we received a commentary by Sjøberg and Bergersen. Given that the topic is of great methodological interest to the community and requires nuanced treatment, we invited two editorial board members, Martin Shepperd and Per Runeson, respectively, to provide additional views
Facilitating feasibility analysis: the pilot defects prediction dataset maker
No full textOur industrial experience in institutionalizing defect prediction models in the software industry shows that the first step is to measure prediction metrics and defects to assess the feasibility of the tool, i.e., if the accuracy of the defect prediction tool is higher than of a random predictor. However, computing prediction metrics is time consuming and error prone. Thus, the feasibility analysis has a cost which needs some initial investment by the potential clients. This initial investment acts as a barrier for convincing potential clients of the benefits of institutionalizing a software prediction model. To reduce this barrier, in this paper we present the Pilot Defects Prediction Dataset Maker (PDPDM), a desktop application for measuring metrics to use for defect prediction. PDPDM receives as input the repository's information of a software project, and it provides as output, in an easy and replicable way, a dataset containing a set of 17 well-defined product and process metrics, that have been shown to be useful for defect prediction, such as size and smells. PDPDM avoids the use of outdated datasets and it allows researchers and practitioners to create defect datasets without the need to write any lines of code
Snoring: A noise in defect prediction datasets
No full textIn order to develop and train defect prediction models, researchers rely on datasets in which a defect is often attributed to a release where the defect itself is discovered. However, in many circumstances, it can happen that a defect is only discovered several releases after its introduction. This might introduce a bias in the dataset, i.e., treating the intermediate releases as defect-free and the latter as defect-prone. We call this phenomenon as 'sleeping defects'. We call 'snoring' the phenomenon where classes are affected by sleeping defects only, that would be treated as defect-free until the defect is discovered. In this paper we analyze, on data from 282 releases of six open source projects from the Apache ecosystem, the magnitude of the sleeping defects and of the snoring classes. Our results indicate that 1) on all projects, most of the defects in a project slept for more than 20% of the existing releases, and 2) in the majority of the projects the missing rate is more than 25% even if we remove 50% of releases
The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects
No full textDefect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of "dormant defects," i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore "snoring." In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction
On effort-aware metrics for defect prediction
No full textContext Advances in defect prediction models, aka classifiers, have been validated via accuracy metrics. Effort-aware metrics (EAMs) relate to benefits provided by a classifier in accurately ranking defective entities such as classes or methods. PofB is an EAM that relates to a user that follows a ranking of the probability that an entity is defective, provided by the classifier. Despite the importance of EAMs, there is no study investigating EAMs trends and validity. Aim The aim of this paper is twofold: 1) we reveal issues in EAMs usage, and 2) we propose and evaluate a normalization of PofBs (aka NPofBs), which is based on ranking defective entities by predicted defect density. Method We perform a systematic mapping study featuring 152 primary studies in major journals and an empirical study featuring 10 EAMs, 10 classifiers, two industrial, and 12 open-source projects. Results Our systematic mapping study reveals that most studies using EAMs use only a single EAM (e.g., PofB20) and that some studies mismatched EAMs names. The main result of our empirical study is that NPofBs are statistically and by orders of magnitude higher than PofBs. Conclusions In conclusion, the proposed normalization of PofBs: (i) increases the realism of results as it relates to a better use of classifiers, and (ii) promotes the practical adoption of prediction models in industry as it shows higher benefits. Finally, we provide a tool to compute EAMs to support researchers in avoiding past issues in using EAMs
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