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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...
Automated builds are integral to the Continuous Integration (CI) software development practice. In CI, developers are encouraged to integrate early and often. However, long build times can be an issue when integrations are frequent. This research focuses on finding a balance between integrating often and keeping developers productive. We propose and analyze models that can predict the build time of...
As machine learning (ML) becomes increasingly popular, developers without deep experience in ML — who we will refer to as ML practitioners — are facing the need to diagnose problems with ML models. Yet successful diagnosis requires high-level expertise that practitioners lack. As in many complex data-oriented domains, visualization could help. This two-phase study explored the design of visualizations...
Software Analytics is gaining momentum as aresult of involved empirical research in enhancing quality andproductivity of software engineering activities. There have beenrigorous research efforts in the areas of bug prediction and testingeffort prediction by making use of historical data. The problemof predicting bug fix times is an interesting problem with lotsof advantages to industry but there have...
In software effort estimation lot of algorithmic and prediction methods are available. Even though the researches try to get a better effort estimation technique. Because it has a vital role in software engineering. Mostly, the industries use the expert's judgements for assessing the effort. The experts may use some methodologies for their references. So, still the algorithmic and prediction methods...
Software effort estimation consists of those procedures and activities which help to predict most accurate development effort as well as cost of a software product. After analyzing various proposed concept and theories regarding this we tried to give a new concept which works over partition of a data set. The partition procedure depends over the correlation of input features as well as output features...
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...
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...
To contribute software testing, and save testing costs, a wide range of machine learning approachs have been studied to predict defects in software modules. Unfortunately, the imbalanced nature of this type of data increases the learning difficulty of such a task. In this paper, we present UCRF, a method based on undersampling technique and conditional random field (CRF) for software defect prediction...
Appearing social networks these days, the capacity of produced information has an increasing growing. The usual learning techniques don't have an efficient performance and the need of utilizing increasing learning methods is seen as a necessary factor. In mining the text in social networks we can see that text mining and social analyzing in texts are new topics in data analyzing which are considered...
Appearing social networks these days, the capacity of produced information has an increasing growing. The usual learning techniques don't have an efficient performance and the need of utilizing increasing learning methods is seen as a necessary factor. In mining the text in social networks we can see that text mining and social analyzing in texts are new topics in data analyzing which are considered...
Managing change in the early stages of a software development life cycle is an effective strategy for developing a good quality software at low costs. In order to manage change, we use software quality models which can efficiently predict change prone classes and hence guide developers in appropriate distribution of limited resources. This study examines the effectiveness of ten machine learning algorithms...
Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization(PSO) and Support Vector Machine(SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing...
High dimensionality and class imbalance are the two main problems affecting many software defect prediction. In this paper, we propose a new technique, named SelectRUSBoost, which is a form of ensemble learning that in-corporates data sampling to alleviate class imbalance and feature selection to resolve high dimensionality. To evaluate the effectiveness of the new technique, we apply it to a group...
Reducing the number of latent software defects is a development goal that is particularly applicable to high assurance software systems. For such systems, the software measurement and defect data is highly skewed toward the not-fault-prone program modules, i.e., the number of fault-prone modules is relatively very small. The skewed data problem, also known as class imbalance, poses a unique challenge...
Decision making under uncertainty is a critical problem in the field of software engineering. Predicting the software quality or the cost/ effort requires high level expertise. AI based predictor models, on the other hand, are useful decision making tools that learn from past projects' data. In this study, we have built an effort estimation model for a multinational bank to predict the effort prior...
Software fault prediction models play an important role in software quality assurance. They identify software subsystems (modules,components, classes, or files) which are likely to contain faults. These subsystems, in turn, receive additional resources for verification and validation activities. Fault prediction models are binary classifiers typically developed using one of the supervised learning...
Associative models are usually applied in knowledge discovery problems in order to find patterns in large databases containing mainly nominal data. This work is focused on two different aspects, the predictive use of association rules and the management of quantitative attributes. The aim is to induce class association rules that allow predicting software size from attributes obtained in early stages...
High assurance software requires extensive and expensive assessment. Many software organizations frequently do not allocate enough resources for software quality. We research the defect detectors focusing on the data sets of software defect prediction. A rough set model is presented to deal with the attributes of data sets of software defect prediction in this paper. Appling this model to the most...
Today's mobile devices have inherited many of the characteristics of desktop computing -- including the assumptions that the user's full attention and dexterity can be focused on the interface. Unfortunately, on-the-go users are impaired by their mobility and often find desktop-style Windows Icon Menu and Pointer (WIMP) interfaces difficult, if not impossible, to use while performing their primary...
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