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Over time, a software system's code and its underlying design tend to decay steadily and, in turn, to complicate the system's maintenance. In order to address that phenomenon, many researchers tried to help engineers predict parts of a system that are most likely to create problems while or even before they are modifying the system. Problems that creep into a system may manifest themselves as bugs,...
Bug prediction has been a hot research topic for the past two decades, during which different machine learning models based on a variety of software metrics have been proposed. Feature selection is a technique that removes noisy and redundant features to improve the accuracy and generalizability of a prediction model. Although feature selection is important, it adds yet another step to the process...
Bug prediction is a technique that strives to identify where defects will appear in a software system. Bug prediction employs machine learning to predict defects in software entities based on software metrics. These machine learning models usually have adjustable parameters, called hyperparameters, that need to be tuned for the prediction problem at hand. However, most studies in the literature keep...
Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes...
Bug Prediction is an important research area in the field of software engineering. Researchers have developed and implemented a number of bug prediction approaches like past bugs, code churn, refactoring, file size and number of authors, etc and measured their performance. Various mathematical models have also been proposed by researchers for monitoring the bug detection and correction process. The...
Code-based metrics and network analysis based metrics are widely used to predict defects in software. However, their effectiveness in predicting bugs either individually or together is still actively researched. In this paper, we evaluate the performance of these metrics using three different techniques, namely, Logistic regression, Support vector machines and Random forests. We analysed the performance...
The Law of Demeter formulates the rule-of-thumb that modules in object-oriented program code should "only talk to their immediate friends". While it is said to foster information hiding for object-oriented software, solid empirical evidence confirming the positive effects of following the Law of Demeter is still lacking. In this paper, we conduct an empirical study to confirm that violating...
Coverage model is the main technique to evaluate the thoroughness of dynamic verification of a Design-under-Verification (DUV). However, rather than achieving a high coverage, the essential purpose of verification is to expose as many bugs as possible. In this paper, we propose a novel verification methodology that leverages the early bug prediction of a DUV to guide and assess related verification...
Dependency network measures capture various facets of the dependencies among software modules. For example, betweenness centrality measures how much information flows through a module compared to the rest of the network. Prior studies have shown that these measures are good predictors of post-release failures. However, these studies did not explore the causes for such good performance and did not...
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