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Fault prediction techniques aim to predict faulty module in order to reduce the effort to be applied in later phase of software development. Majority of the approaches available in literature for fault prediction are based on regression analysis and neural network techniques. It is observed that numerous software metrics are also being used as input for fault prediction. In this paper, a cost evaluation...
Faulty modules of any software can be problematic in terms of accuracy, hence may encounter more costly redevelopment efforts in later phases. These problems could be addressed by incorporating the ability of accurate prediction of fault prone modules in the development process. Such ability of the software enables developers to reduce the faults in the whole life cycle of software development, at...
Dynamic data structures in software applications have been shown to have a large impact on system performance. In this paper, we explore energy saving opportunities of interface-based dynamic data structures. Our results suggest that opportunities do exist in the C5 Collection, at least 16.95% and up to 97.50%. We propose an architecture for building adaptive green data structures by applying machine...
Product backlog comprises the outline of desired functionality in a prioritized order in Scrum. Scrum is an iterative, collaborative, and flexible process to develop a software product. Scrum projects welcome changes at any stage of a project, but such changes significantly affect project management activities and quality, including time and budget overrun. These changes also compromise architectural...
A recent study namely software defect prediction model based on Local Linear Embedding and Support Vector Machines (LLE-SVM) has indicated that Support Vector Regression (SVR) has an interesting potential in the field of software defect prediction. However, the parameters optimization of LLE-SVM model is computationally expensive by using the grid search algorithm, resulting in a lower efficiency...
Reliability is a software quality characteristic that refer to the probability a system will work correctly over a period of time. Reliability prediction is important as it can be used to plan deployment, maintenance and test activities. This study assesses the efficiency of several techniques in software reliability model (SRM) selection and aims to find out the possible enhancement to improve software...
Researchers have found that approximately 70% of information systems development projects in Japan have failed, thus increasing the demand for solutions that will raise expected project success rates. It is said that to improve success rates, it is essential that risk management should be conducted at an early stage. Although risk management is an important process that a project focuses on, it is...
Vulnerabilities usually represents the risk level of software, and it is of high value to forecast vulnerabilities so as to evaluate the security level of software. Current researches mainly focus on predicting the number of vulnerabilities or the occurrence time of vulnerabilities, however, to our best knowledge, there are no other researches focusing on the prediction of vulnerabilities' severity,...
Software projects have a high risk of cost and schedule overruns, which has been a source of concern for the software engineering community for a long time. One of the challenges in software project management is to make reliable prediction of delays in the context of constant and rapid changes inherent in software projects. This paper presents a novel approach to providing automated support for project...
Self-awareness has a long history in biology, psychology, medicine, and more recently in engineering and computing, where self-aware features are used to enable adaptivity to improve a system's functional value, performance and robustness. With complex many-core Systems-on-Chip (SoCs) facing the conflicting requirements of performance, resiliency, energy, heat, cost, security, etc. - in the face of...
Defect prediction on new projects or projects with limited historical data is an interesting problem in software engineering. This is largely because it is difficult to collect defect information to label a dataset for training a prediction model. Cross-project defect prediction (CPDP) has tried to address this problem by reusing prediction models built by other projects that have enough historical...
Despite the development of several churn prediction software that investigate hundreds of factors, there is still much space for improving the accuracy of churn prediction. This explorative case study research investigated the behavioral factors, economic factors, and carrier policies causing churn and revealed a new set of important factors that cause churn that should be incorporated into churn...
In spite of presence of many classical and modified data analysis techniques, data analysis in the field of software engineering still remains a challenge because of the presence of large number of both continuous and discreet explanatory variables judging the outcome of one and more than one dependant variables. Requirement for an efficient multivariate data analysis technique which fulfils the constraints...
[Context]: The numerous challenges that can hinder software companies from gathering their own data have motivated over the past 15 years research on the use of cross-company (CC) datasets for software effort prediction. Part of this research focused on Web effort prediction, given the large increase worldwide in the development of Web applications. Some of these studies indicate that it may be possible...
Context: Software source code is frequently changed for fixing revealed bugs. These bug-fixing changes might introduce unintended system behaviors, which are inconsistent with scenarios of existing regression test cases, and consequently break regression testing. For validating the quality of changes, regression testing is a required process before submitting changes during the development of software...
In this paper, we present the concept of data science foundry for data from Massive Open Online Courses. In the foundry we present a series of software modules that transform the data into different representations. Ultimately, each online learner is represented using a set of variables that capture his/her online behavior. These variables are captured longitudinally over an interval. Using this representation...
Software defect (Bug) prediction plays an important role in improving software quality. Many software defect prediction approaches have been proposed and achieved great effects in the real-world. However, the existing works are usually constrained in only one project, hence their effectiveness on cross-project defect prediction (cross-prediction) is usually poor. This is mainly because of the problem...
The automated testing of Web-based software can drastically improve the quality and reduce the costs of software development and testing. To make it more efficient we should analyze the habits and preferences of end-users. Here we analyze the known approaches to users' social habits analysis, and their applications for improving the efficiency of testing of Web applications, taking as an example the...
We review strategies for applying statistical inference to system design and management. In design, inferred models act as surrogates for expensive simulators and enable qualitatively new studies. In management, inferred models predict outcomes from allocation and scheduling decisions, and identify conditions that make performance stragglers more likely.
Using software testing data collected, this paper established a software safety defects S curve model based on Weibull model theory. χ2 testing and prediction error testing are employed to verify the matching ability of the Weibull model and applicability of the predicting result. How to select truncation error is also discussed here. Results show that better predictive effect can be achieved...
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