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The software composition using high-granularity entities nowadays is a common practice. The process of software composition is supported by various CASE tools. First tools were made on the basis of very simple formalisms (e.g. intuitionistic propositional logic). During the years the tools evolved to more efficient ones, which are able to deal with concurrency, multiparty sessions and other advanced...
New software applications for linear multivariable system identification are presented. The incorporated algorithms use subspace-based techniques (MOESP, N4SID, or their combination) to find a standard discrete-time state-space description, and optionally the covariance matrices and Kalman predictor gain, using input and output (I/O) trajectories. For flexibility, separate applications are offered...
Determinism is a key concern in the certification of software for safety-critical systems. In this paper, we evaluate the role of determinism in certification standards, using airborne software as example. We analyze and speculate how the requirements and underlying concepts related to determinism can be adapted for Machine Learning algorithms.In addition, we systematically identify and analyze a...
We develop a cache-efficient RNA folding algorithm, ByBox, that is based on Zuker's method. Using a simple LRU cache model, we show that the traditional implementation, Zuker, of Zuker's method has a much higher number of cache misses than ByBox. Extensive experiments conducted on the Xeon E5 server show that cache efficiency translates into time and energy efficiency. Our benchmarking shows that,...
Integrated development environments (IDEs) are complex applications that integrate multiple tools for creating and manipulating software project artifacts. To improve users' knowledge and the effectiveness of usage of the available functionality, the inclusion of recommender systems into IDEs has been proposed. We present a novel IDE command recommendation algorithm that, by taking into account the...
Predicting the number of defects in software modules can be more helpful in the case of limited testing resources. The highly imbalanced distribution of the target variable values (i.e., the number of defects) degrades the performance of models for predicting the number of defects. As the first effort of an in-depth study, this paper explores the potential of using resampling techniques and ensemble...
Finding suitable developers for a given task is critical and challenging for successful crowdsourcing software development. In practice, the development skills will be improved as developers accomplish more development tasks. Prior studies on crowdsourcing developer recommendation do not consider the changing of skills, which can underestimate developers' skills to fulfill a task. In this work, we...
Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are...
The highly imbalanced nature of software fault datasets results in poor performance of machine leaning techniques used for software fault prediction. The objective of this paper is to evaluate sampling techniques and Meta-Cost learning in software fault prediction to alleviate problem of imbalanced data. We evaluate four sampling techniques in metrics as well as code smells based fault prediction...
An essential attribute of the software quality is maintainability which incurs almost 60–70% of total project cost. Since software maintainability prediction is a complicated process; estimating maintainability in the prior phases of software development lifecycle (SDLC) is advantageous. Further, it helps in building economical software and improving resource planning well in advance. Software metrics...
Good quality software is a supporting factor that is important in any line of work in of society. But the software component defective or damaged resulting in reduced performance of the work, and can increase the cost of development and maintenance. An accurate prediction on software module prone defects as part of efforts to reduce the increasing cost of development and maintenance of software. An...
Link prediction has become an important research topic in the field of complex networks. The purpose of link prediction is to find the missing links or predict the emergence of new links that do not present currently in a complex networks. Considering that the local centrality of common neighbor nodes have an important effect on the similarity-based algorithm, but every centrality measure has its...
Although there have lots of studies on using static code attributes to identify defective software modules, there still have many challenges. For instance, it is difficult to implement the Apriori-type algorithm to predict defects by learning from an imbalanced dataset. For more accurate and understandable defect prediction, a novel approach based on class-association rules algorithm is proposed....
Traction power supply system, as the only power source of high-speed railway, the safety and reliability of its equipment's operation are very important. The effective integration and usage of the operation data of traction power supply equipment has been a hot research topic during recent years. In order to improve the utility of equipment's operation data, it is necessary to build a comprehensive...
Providing the optimal configuration for a software router poses a lot of technical challenges that do not present in the dedicated hardware router. One of them is how to characterize performance varying due to different configurations on commodity hardware. This paper addresses the problem of configuring a software router that provides the minimum of average packet latency. Since changing all combinations...
Multi-label text classification plays a significant role in information retrieval area. The effectiveness of the techniques is especially important in the case of medical documents. In the paper, application of feature selection methods for improving multi-label medical text classification is discussed. We examine combining problem transformation methods with different approaches to feature selection...
The mobile application market and e-commerce sales have grown steadily, along with the growth of studies and product recommendation solutions implemented in e-commerce systems. In this context, this paper proposes a recommendation algorithm for mobile devices based on the COREL (Customer Purchase Prediction Model) framework. The proposed recommendation algorithm is a customization of the COREL framework,...
In this short paper, we compare well-known rule/tree classifiers in software defect prediction with the CTC decision tree classifier designed to deal with class imbalanced. It is well-known that most software defect prediction datasets are highly imbalance (non-defective instances outnumber defective ones). In this work, we focused only on tree/rule classifiers as these are capable of explaining the...
This paper discusses three case studies involving the use of feature subset selection algorithms, as well as feature ranking, based on real data on student performances in a course of a Bachelor of Computer Science program. The case studies aimed at investigating, as a step prior to the use of data mining algorithms for performance prediction, the effectiveness of feature selection methods, for reducing...
In the last few years, software aging has been reported. The phenomenon of software aging is that the performance of the software system is degradation in a long-running state, which is the result of exhaustion of system resources, the accumulation of internal error conditions and so on. In order to counteract software aging, a technique, which called software rejuvenation, has been proposed, this...
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