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With the development of software reliability research and machine learning, many machine learning models have been used in software reliability prediction. A long short term memory network (LSTM) modeling approach for software reliability prediction is proposed. Profit from its particular data flow control structure, the model overcomes the vanishing and exploding sensitivity of simple recursive neural...
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...
Several defect prediction models proposed are effective when historical datasets are available. Defect prediction becomes difficult when no historical data exist. Cross-project defect prediction (CPDP), which uses projects from other sources/companies to predict the defects in the target projects proposed in recent studies has shown promising results. However, the performance of most CPDP approaches...
With the expanding load on healthcare and consequent strain on budget, the demand for tools to increase efficiency in treatments is rising. The use of prediction models throughout the treatment to identify risk factors might be a solution. In this paper we present a novel implementation of a prediction tool and the first use of a dynamic predictor in vocational rehabilitation practice. The tool is...
Too often, capacity planning activities that are crucial to software performance are being pushed to late development phases where trivial measurement-based assessment techniques can be employed on enterprise applications that are nearing completion. This procedure is highly inefficient, time consuming, and may result in disproportionately high correction costs to meet existing service level agreements...
In the paper, there are considered functional abilities of information operator support systems (IOSS) of power units of nuclear power plants (NPP) and their place within the an unit-level system of automated process control systems (APCS) of NPP. The purpose of implementation of NPP IOSS is preventing or decreasing the frequency and heaviness of human-operator errors appearing during transients and...
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,...
Syslogs on switches are a rich source of information for both post-mortem diagnosis and proactive prediction of switch failures in a datacenter network. However, such information can be effectively extracted only through proper processing of syslogs, e.g., using suitable machine learning techniques. A common approach to syslog processing is to extract (i.e., build) templates from historical syslog...
The paper addresses implementation of advanced predictive control (APC) for a distillation column. The APC controller was designed using Profit Design Studio software. The distillation column was modeled and the closed-loop control was implemented in UniSim Design environment. The distillation column was handled as a multiple-inputs and multiple-outputs system. Moreover, constraints on the controlled...
Performance prediction is critical in embedded system design for reducing the turnaround time of software. Using simulation to measure the performance of the whole source code is often too slow, particularly after the modification of the source code due to changes in problem specification. In this paper we present a comprehensive method that combines analytical modeling and statistical approach to...
Due to the constant evolution of technology, each day brings new programming languages, development paradigms, and ways of evaluating processes. This is no different with source code metrics, where there is always new metric classes. To use a software metric to support decisions, it is necessary to understand how to perform the metric collection, calculation, interpretation, and analysis. The tasks...
Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific...
Sequence to sequence (seq2seq) prediction is a key to many tasks of machine learning. Personal computer software sequence, as one of these tasks, was regarded as stochastic and unpredictable in the past. However, the deep neural networks (DNNs) have achieved excellent performance recently in sequence to sequence tasks, especially in the field of natural language process (NLP) such as language model,...
This paper presents an adaptive video encoder that can be used to compare the behavior of different adaptation strategies using multiple actuators to steer the encoder towards a global goal, composed of multiple conflicting objectives. A video camera produces frames that the encoder manipulates with the objective of matching some space requirement to fit a given communication channel. A second objective...
Background: Due to tight scheduling and limitedbudget, it may not be possible to resolve all the existing bugsin a current release of a software product. The accumulation ofthe deferred bugs in the issue tracking system are obligations (liabilities) of the software team similar to financial analogyof 'debt'. Defect debt is known as latent defects which arenot resolved in the current release. Aim:...
Due to the increased importance of machine learning in software and security engineering, effective trainings are needed that allow software engineers to learn the required basic knowledge to understand and successfully apply prediction models fast. In this paper, we present a two-days seminar to teach machine learning-based prediction in software engineering and the evaluation ofits learning effects...
Build systems play a crucial role in modern software engineering. Recent studies have shown that many builds fail, mostly due to neglected maintenance. This blocks teams from continuing the development and costs time and resources to fix. The target of the thesis is to reduce build breakage by investigating changes that lead to failing builds, identifying bad and best practices for build configuration,...
Researchers often focus on the development process and the final product (source code) to investigate and predict software defects. Unfortunately, these models may not be applicable to software projects in which there is no access to the data sources regarding development process. For example, in cases when a company conducts tests on behalf of its business contractors, it is only possible to evaluate...
We would like to present the idea of our Continuous Defect Prediction (CDP) research and a related dataset that we created and share. Our dataset is currently a set of more than 11 million data rows, representing files involved in Continuous Integration (CI) builds, that synthesize the results of CI builds with data we mine from software repositories. Our dataset embraces 1265 software projects, 30,022...
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