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Code review is known to be of paramount importance for software quality assurance. However, finding a reviewer for certain code can be very challenging in Modern Code Review environment due to the difficulty of learning the expertise and availability of candidate reviewers. To tackle this problem, existing efforts mainly concern how to model a reviewer's expertise with the review history, and making...
Continuous Integration(CI) is a widely used development practice where developers integrate their work after submitting code changes at central repository. CI servers usually monitor central repository for code change submission and automatically build software with changed code, perform unit testing, integration testing and provide test summary report. If build or test fails developers fix those...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
The number of software vulnerabilities discovered and publicly disclosed is increasing every year; however, only a small fraction of them is exploited in real-world attacks. With limitations on time and skilled resources, organizations often look at ways to identify threatened vulnerabilities for patch prioritization. In this paper, we present an exploit prediction model that predicts whether a vulnerability...
Background: Software defect models can help software quality assurance teams to allocate testing or code review resources. A variety of techniques have been used to build defect prediction models, including supervised and unsupervised methods. Recently, Yang et al. [1] surprisingly find that unsupervised models can perform statistically significantly better than supervised models in effort-aware change-level...
Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate...
User-generated mobile application reviews have become a gold mine for timely identifying functional defects in this type of software artifacts. In this work, we develop a hidden structural SVM model for extracting detailed defect descriptions from user reviews at the sentence level. Structured features and constraints are introduced to reduce the demand of exhaustive manual annotation at the sentence...
Background: An increasing research effort has devoted to just-in-time (JIT) defect prediction. A recent study by Yang et al. at FSE'16 leveraged individual change metrics to build unsupervised JIT defect prediction model. They found that many unsupervised models performed similarly to or better than the state-of-the-art supervised models in effort-aware JIT defect prediction. Goal: In Yang et al.'s...
Future of food innovation lies in the art and science of designing an interactive connected intelligent device that can detect how we feel and display the content suitable for individual consumers. We designed a smart dining table and chairs that can detect, sense, and analyze consumer's satisfaction, and interact with consumers. A team of furniture designer, software engineers, mechanical engineer,...
The aim of this paper is to introduce a semantic methodology using ontology in order to improve results of data mining in judicial decisions database. An intelligent and automatic method to search for sentences in lawsuits related to the one in trial is presented. A judicial ontology is built with and without rules from experts. The method can provide judiciary celerity, seeking to solve the yearning...
This paper presents a generalized modeling framework of fault detection and correction processes with bivariate distributions. The presented framework includes almost all existing software reliability growth models, namely the models in which both fault detection and correction processes are described by non-homogeneous Poisson processes. In our framework, the time dependency of fault correction time...
The technology of 2D GM-SYS gravity-magnetic inversion improved the reliability of geological sections, but the result of inversion can’t be imported into the GoCAD software to build the 3D geological model directly. A software, named G2G, is developed based on the file formats of GM-SYS and GoCAD, to transfer the GM-SYS inversion sections into GoCAD software format’s files, improving the efficiency...
The assurance of autonomous systems and the technologies that drive them is a major research challenge in the safety-critical systems engineering domain. The nature of many of these Machine Learning (ML) and Artificial Intelligence (AI) approaches raises a number of additional, technology-specific assurance concerns. One such approach is the Bayesian Network (BN) probabilistic modelling framework...
A good methodology flow can improve significantly the time needed to develop an I/O library. It is mandatory that the fastest methodology also gives accurate results. Given the new technology nodes, it is important to re-evaluate all the software characterization formats (NLDM, CCS and ECSM) and see if there is room for improvement. The analysis will be conducted by comparing liberty file data with...
Most view-based modelling approaches are today based on a "synthetic" approach in which the views hold all the information modelled about a system and are kept consistent using explicit, inter-view correspondence rules. The alternative "projective" approach, in which the contents of views are "projected" from a single underlying model on demand, is far less widely used...
Conceptual elevation of data in business process modeling was first formulated in 2003. The research community responded to this new idea enthusiastically. In the past decade, there have been numerous research activities concerning the interactions between business processes/activities and data in many aspects of business process management. Many of the advancements have or will have impacted on design/modeling,...
In modern manufacturing, large data sets from different sources are permanently generated along the production chain. This data are supposed to be used to optimize products and production chains. However, in most cases, process participants only focus on acquiring data and leave it then to decision makers for interpretation. While there is nothing wrong with that on principle, comparable results might...
The use of an Application Lifecycle Management (ALM) system to promote cross-discipline data capture, tracking and traceability has shown to provide a dramatic improvement during airworthiness audits of software-centric avionics systems by reducing both the time to locate relevant information and rework associated with errors in captured data and their traceability. When configured correctly, an ALM...
The increasing amount of gathered sensor data in Industry 4.0 allows comprehensive data analysis software that creates value-adding opportunities. As companies often cannot implement such software by themselves and as they typically don't want to give their data to external scientists, they commission them to build the required software in order to execute it locally. However, installing, configuring,...
Distributed Denial of Service (DDoS) attack has been bringing serious security concerns on banks, finance incorporation, public institutions, and data centers. Also, the emerging wave of Internet of Things (IoT) raises new concerns on the smart devices. Software Defined Networking (SDN) and Network Functions Virtualization (NFV) have provided a new paradigm for network security. In this paper, we...
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