It is quite challenging to track the after-effects of changes with increased dependencies among classes while making a change in software applications. Software change impact analysis aims to identify classes affected by a change in software applications. In recent years, researchers have found that revision history has great potential for identifying evolutionary coupling. The two main factors affecting the prediction results are the length and the age of change history considered for deriving dependencies. The effect of age of change history on co-change prediction results in software applications is empirically studied by varying the weightage of change commits. The results indicate that the older commits have less influence in deriving dependent classes than the newer ones. The proposed approach is useful for software effort estimation and identifying dependencies during the software development, testing, and maintenance phase.

Bug assignment (BA), the process of ranking developers according to their potential ability to fix a given bug, is an important software-engineering task. BA usually requires the development of an expertise profile for each developer, and formulation of a similarity metric to estimate the relevance of developers to the bug. This needs us to answer the following question: ‘what is the information value of various contributions of developers in BA research?’ We address this question by making the following contributions. (i) We enhance the expertise metric of our prior work, vocabulary and time-based BA, to consider information regarding various sources of expertise with different importance. We show that this can improve the effectiveness of bug-assignment process. (ii) Using this ‘Multisource’ expertise metric, we investigate the information value of different pieces of information in open-source repositories for BA. We show that in addition to bug-fixing contributions, other technical and even social contributions of developers within the version-control system are useful information for BA. (iii) We provide a curated, up-to-date data set including technical information of 13 popular open-source projects in Github. To the best of our knowledge, this is the most comprehensive data set, currently available for bug-assignment research.

In software crowdsourcing, task prize is a primary incentive for engaging crowd developers. One of the main challenges in crowdsourcing task pricing is to determine appropriate prizes in order to attract qualified workers. Few studies proposed methods to address this challenge. However, they are either too theoretical or too restricted to be applied for early crowdsourcing planning. In this study, we propose a novel approach, i.e., PTMA, to support early task pricing in software crowdsourcing from textual task requirements. PTMA consists of three phases, namely data pre-processing, topic extraction, and topic-based task pricing analysis, integrating 6 machine learning algorithms and 3 analogy-based models for topic-based pricing analysis. PTMA is evaluated using data from 2016 software crowdsourcing tasks extracted from TopCoder, the largest software crowdsourcing platform. The results show that: 1) textual requirement information can aid early task pricing in software crowdsourcing; 2) the best predictor in PTMA, based on logistic regression, achieves an accuracy of 88.3% in Pred (30); and 3) PTMA outperforms the existing baseline models by 9% in Pred (30). PTMA greatly simplifies the pricing process by only leveraging textual task description as inputs, and can achieve better prediction accuracy in making task pricing decisions.

Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high-quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K-PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k-fold cross-validation to avoid the overfitting problem. They found ELM-based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F-score values over ten and nine, respectively, compared with other state-of-the-art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.

Change of artefact-centric business process instances is important for an enterprise to keep competitive. However, to preserve the correctness of changes of an artefact-centric business process instance is still a big challenge. The hardness mainly stems from the fact that whether a business process instance can reach a final state has been proved to be undecidable. As such, finding a reliable verification algorithm for preserving correctness becomes impossible. In this study, the authors propose a random-forest-based approach to predict the correctness of changes in a business process instance. The availability of the proposed method is validated by comparing it to the traditional formal verification method. They also propose two optimisations of the proposed method and validate their effectiveness through extensive experimental analysis.