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.

Code review is a widely adopted means of improving software quality. There are numerous techniques of reviewing including tool-assisted and over-the-shoulder. Both require significant effort to perform. However, the impact the over-the-shoulder has on review effectiveness remains unexplored. The authors goal was to compare the techniques and the effect of review size to analyse how that affects the effectiveness. They performed three experiments within one company. Changes provided by developers were reviewed twice by different reviewers, each time using a different technique. Afterwards, participants rated in a questionnaire the influence of each technique to the knowledge transfer. They observed that the number of accepted comments reported with the tool-assisted technique is approximately twice as big. Also, they noticed a relationship between comments density and the review size: the correlations vary from  − 0.42 to  − 0.33. The knowledge transfer was typically evaluated as it supports knowledge sharing to a limited extent for the tool-assisted technique but as it is good at supporting knowledge sharing for the over-the-shoulder technique. The results did not give a simple answer whether one technique outperforms the other. The tool-assisted technique results in more comments. However, the teams evaluated the over-the-shoulder technique as better supporting knowledge transfer.

The incorporation of suitable external data from the World Wide Web offers an effective solution for enriching the data in the data warehouse (DW). However, the main challenge is the quality-aware selection of web data sources to maintain the quality of the DW. In the previous works, the quality evaluation of web sources is through expert evaluation only, which makes it a very lengthy process. Also, since the quality model consists of mixed quality factors from diverse domains of Web, DW and underlying business, finding an expert possessing an expertise of all these domains is a huge bottleneck in the evaluation process. In order to overcome these existing issues, this study proposes a novel multi-level approach web source evaluation with multi-criteria decision-making and web quality testing tools (WSEM_QT) and underlying quality model web quality model for evaluating web sources for the DW. The authors introduce automated web source quality evaluation in the first level of web source based evaluation and multiple dimensions of quality evaluation at the second level of expert-based evaluation. At both the levels, multi-criteria decision-making methods are applied to the evaluation scores obtained to ascertain the ranked list of Web sources. The authors present a real-world academic web data case study which shows that the proposed approach can be executed successfully for real-world problems.

Recently, transforming graphical user interface (GUI) mockups into code becomes a common challenging practice for current software developers. However, this transformation usually takes time especially when GUI changes keep pace with evolutionary features. There are many studies admitted this challenge and presented solutions in terms of computer-based GUI mockups. However, there is a research gap in this kind of research as very few of them adopted hand-drawn mockups as an input. In this study, the authors employed YOLOv5 is a fast and accurate deep learning framework to automate the process of converting hand-drawn GUI mockups into Android-based GUI prototype. The process starts with detecting all GUI mockups in an input image and determining their bounding boxes, classifying these mockups into their corresponding GUI objects, then finally aligning these objects together to form the output prototype based on the layout presented in the input image. Experimental results show the effectiveness of the proposed approach in generating a visually appealing Android GUI from hand-drawn mockups with a recognition accuracy of 98.54% when tested on various hand-drawn GUI structures designed by five developers.

Cross-project defect prediction (CPDP) technology refers to the constructing prediction model to predict the instance label of the target project by utilising labelled data from an external project. The challenge of CPDP methods is the distribution difference between the data from different projects. Transfer learning can transfer the knowledge from the source domain to the target domain with the aim to minimise the domain difference between different domains. However, most existing methods reduce the distribution discrepancy in the original feature space, where the features are high-dimensional and non-linear, which makes it hard to reduce the distribution distance between different projects. Moreover, previous works mainly consider marginal distribution or conditional distribution difference. In this study, the authors proposed a manifold embedded distribution adaptation (MDA) approach to narrow the distribution gap in manifold feature subspace. MDA maps source and target project data to manifold subspace and then joint distribution adaptation of conditional and marginal distributions is performed on manifold subspace. To evaluate the effectiveness of MDA, the authors perform extensive experiments on 20 public projects with three indicators. The experiment results show that MDA improves the average performance, but the improvement is not statistically significant in comparison to HYDRA (one of the baselines).

Software development is extremely complex, requiring collaboration between teams and developers who collaborate on various tasks; these activities lead to the generation of an implicit developer social network (DSN). The authors’ aim to understand the development process in terms of collaboration between developers. In this work, they conducted an empirical study on mining social collaboration patterns of DSNs for open source software projects based on an integrated approach involving the identification of global and local collaboration patterns among developers based on social network analysis. The bug tracking system-based DSN (BTS-DSN) is chosen as an example over the other DSNs since it incorporates larger collaboration activities and actors. The empirical results show that the DSNs, specifically BTS-DSN, exhibits three different coordination pattern levels (Plan, Aware, and Reflexive) based on their collaboration activities. The mean time to repair metric proves that the Reflexive level occupies the fastest bug fixing time, then the Plan level comes secondly, and lastly the Aware level. In addition, each level group shows different collaboration behaviours among developers; thus, this information can be useful as a resource for better understanding of developer collaboration and collaboration awareness.

Open Source Software (OSS) has become ubiquitous nowadays. It is crucial for the OSS project managers as well as developers to understand users' perception of quality to remain consistent with producing good quality software. To understand users' point of view, like many studies in the commercial product/service sectors which rely upon customer reviews to understand customer behaviour, the authors' main focus is to analyse user ratings and reviews of OSS projects that may represent user satisfaction for that particular software application. We have analysed 41,428 customer reviews (obtained from SourceForge.net) of 886 most popular OSS projects belonging to a specific domain and programming language. The results indicate that overall user ratings and reviews of the popular OSS projects contain a very positive sentiment and more frequent occurrence of emotions like joy, anticipation, and trust as compared to disgust, fear, and surprise. Further, we have examined that the affectiveness of customer reviews with respect to OSS popularity and quality aspects along with their programming languages and problem domains. The results show a stronger association of review affectiveness with the number of reviews than with the number of downloads of the OSS projects, and more downloads do not mean more reviews.

Agile methods are associated with values, principles, and practices that influence organisational climate. Organisational climate is the meaning employees attach to the policies, practices, and procedures they experience and the behaviours they observe getting rewarded, supported, and expected. A negative climate can influence team members on aspects such as motivation, ability to make good decisions, and willingness to innovate. Conversely, a positive climate can influence project success and the competitiveness of organisations. The authors investigate how organisations considered assessing the organisational climate of Agile teams, and which are the benefits and the difficulties associated with this assessment. They conducted a qualitative study on five Brazilian organisations. They interviewed key personnel involved with organisational climate assessments. They identified 16 benefits and nine difficulties of organisational climate assessments. Based on the results and the literature, they formulated two propositions representing pitfalls that can hinder organisational climate assessments of Agile teams. Organisations should use instruments adapted to the Agile culture to improve the ability to diagnose the organisational climate and involve the Agile team in the climate management activities to ease the identification of the team's perception about project performance and product quality indicators.