Design pattern is a reusable solution to a commonly occurring design problem in certain context. Using design patterns in software development improves the product's quality, understandability and productivity. However, it is a challenging task for novice developers to select the right design pattern to solve a design problem. The paper proposes a methodology for the automatic selection of the fit design pattern from a list of patterns. The proposed methodology is based on Text retrieval approach where the design problem scenarios are described in natural language. A vector space model (VSM) was created for the catalogue of design patterns. A vector of features consists of unigrams and bigrams is generated for the given design problem scenario. The recommended design pattern is the closest to the problem scenario. The proposed mechanism was evaluated using the Gang of four design patterns and the experimental results showed the effectiveness of the proposed methodology.

No abstract

“Severity” is one of the essential features of software bug reports, which is a crucial factor for developers to decide which bug should be fixed immediately and which bug could be delayed to a next release. Severity assignment is a manual process and its accuracy depends on the experience of the assignee. Prior research proposed several models to automate this process. These models are based on textual preprocessing of historical bug reports and classification techniques. Although bug repositories suffer from severity class imbalance, none of the prior studies investigated the impact of implementing a class rebalancing technique on the accuracy of their models. In this paper, we propose a framework for predicting fine-grained severity levels which utilizes an over-sampling technique “SMOTE”, to balance the severity classes, and a feature selection scheme, to reduce the data scale and select the most informative features for training a [Formula: see text]-nearest neighbor (KNN) classifier. The KNN classifier utilizes a distance-weighted voting scheme to predict the proper severity level of a newly reported bug. We investigated the effectiveness of our proposed approach on two large bug repositories, namely Eclipse and Mozilla, and the experimental results showed that our approach outperforms cutting-edge studies in predicting the minority severity classes.

No abstract