Just-in-time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just-in-time defect prediction. Therefore, the authors propose a novel just-in-time defect prediction model named DAECNN-JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation-learning technique, convolution neural network, to construct the basic change features into the abstract deep semantic features. To evaluate the performance of the DAECNN-JDP model, they conduct extensive within-project and cross-project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN-JDP on five evaluation metrics.

In bug fixing process, estimating the ‘Time to Fix Bug’ is one of the factors that helps the triager to allocate jobs in a better way. Due to the limitation of resources for bug fixing, the bugs with long fixing time must be identified, as soon as possible, after receiving the report. This helps the prioritisation and fixing process of the bug reports. In the process of bug fixing, a temporal sequence of activities is done. Each activity is represented by a term. Useful semantic information and long-term dependency are available between terms in the sequence, but it is usually underutilised by existing bug fixing time predictor approaches. This work presents a novel deep learning-based model (called DeepLSTMPred) that (i) converts constituent terms to a vector of real numbers by considering their semantic meaning, (ii) finds the long-term dependencies between terms by deep long short term memory (LSTM) and (iii) classifies sequences to short fixing time or long fixing time. DeepLSTMPred is evaluated on bug reports extracted from the Mozilla project. The results show that the proposed method has better performance in comparison with a state-of-the-art approach (that is the hidden Markov-based model). The experimental results show that DeepLSTMPred achieves 15–20% improvement in terms of accuracy, precision, f-score, and recall.

The field of software development is growing rapidly and prevailing in every walk of life. The role of software developers in such a challenging and complex activity is very much important. The allocation of right software developers (i.e. who possesses appropriate coding skills) to projects is one of the crucial factors for successful software development. The problem is that it is very difficult for a client, project manager, as well as for software development organisations to find out an appropriate developer and assign him/her to a particular project. To achieve this, there is a need for such a sound mechanism that could detect the level of software developer coding expertise. This study has formulated criteria for novice and expert developers and carried out such criteria to discover the level of coding expertise of software developers using three different models of deep learning. These models include long short-term memory (LSTM), convolution 1D and hybrid (a combination of LSTM and convolution 1D). The deep learning models have analysed software developers’ previously written source code collected from the GitHub repository. An experiment was conducted to evaluate the performance of models. The results showed that the LSTM model performed better in comparison to other models by achieving 96.25% accuracy.

The number of Android applications using location information has increased significantly in recent years. Over time, there have been many improvements made to the location application programme interfaces (APIs), providing newer challenges and difficulties to the developers. Therefore, there is a need to summarise the existing knowledge and to highlight the unsolved issues to bring them to the attention of expert developers. The authors used the non-negative matrix factorisation (NMF) method to identify the topics discussed by the developers on stack overflow. They found the following ten topics: fundamental, background service, global positioning system (GPS) provider, application error, location updates, programming aspects, GPS alternatives, location settings, NULL location, and location testing. In addition, they performed a manual analysis to add more qualitative insights into the results. They applied the NMF method on 3165 question posts and produced ten related topics. This study aims at organising the knowledge about location-sensing strategies by answering three relevant research questions. They also analysed the most popular and unanswered topics in recent years. An important finding of this study is that the changes that occurred in the Google Location APIs have had a significant impact on the location-sensing strategies followed by the developers.

Software Development is a complex process, in which every software product is a knowledge representation of all the involved people. In agile software development, knowledge is prone to vaporise, because documentation is not a priority as indicated in the agile manifesto. This condition generates problems such as poor understanding of the requirements, knowledge transfer deficiency among developers, time wasted by developers while searching for knowledge. The objective of this work is to reduce architectural knowledge vaporisation by means of knowledge condensation to support expertise location (high-level knowledge at a given time). This through an ontology that will condensate the knowledge in the code phase. This study presents the description of an ontology development process following the Methontology Framework. Results show that the proposed ontology does not present incongruence or inconsistency and answers the competency questions correctly. The main contribution of this study is the ontology which brings several benefits such as a shared concept of the knowledge in the code phase and a way to link the artefacts (resources used by developers in the project) and the experts (artefacts provider).

Online discussion forums can be used for reflecting on the overall user experience of a system. If a user forum is well-structured, it can be a valuable source of requirements-related information, which can potentially be accommodated in the requirements engineering process to enhance the current and future software. However, presently, there are limited approaches for extracting such requirements-related information from the relevant community forums. To fill this gap, this study proposes an automated approach, which automatically identifies requirements information using natural language processing and machine learning. For this purpose, the authors analysed 3319 user comments collected from the seven discussion topics in the Reddit forum. Then, using a content analysis approach, they studied how frequently end-users submit such information across each discussion topic. Also, they developed an automated approach that identifies key stakeholders, who frequently contribute his rationales in the forum discussion. Further, they employed different machine learning algorithms to classify user comments into rationale elements of different types. The authors' results show that online forums, such as Reddit, can be a rich source of requirements elicitation. Also, machine learning is a promising tool to detect user's rationale and identify different kinds of requirements modelling elements.

Evolution of technology has brought a revolution in various fields of sciences and amongst them, healthcare is one of the most critical and sensitive areas because of its connection with common masses' quality of life. The notion of integrating the healthcare system with the latest data repositories is to make disease prediction efficient, transparent, and reusable. Due to data heterogeneity, data repositories along with optimum classifiers help stakeholders to predict the disease more accurately without compromising the interpretability. Evolutionary algorithms have shown great efficacy, accuracy, and interpretability in improving disease prediction for several datasets. However, the quest for the best classifier is still in evolution. In this research, a state-of-the-art medical data repository has been developed to give researchers of medical domain great ease of use in utilizing different datasets governed by a multi-objective evolutionary algorithm using fuzzy genetics. The proposed model called ‘MEAF’ is evaluated on various public repositories. A subset of these repositories includes breast cancer, heart, diabetes, liver, and hepatitis datasets. The results have been analyzed, which show competitive accuracy, sensitivity, and interpretability as compared to relevant research. A customised software application named ‘MediHealth’ is developed to supplement the proposed model that will facilitate the domain users.

Requirements change management (RCM) is an important and complicated phase in the agile for the software development process. The objective of this study was to identify and analyse the factors that can positively affect the agile RCM (ARCM) process in the context of global software development (GSD). To this end, a questionnaire survey was carried out with researcher and practitioner participants working in the field of GSD. From the 56 survey respondents, a total of 20 factors that can positively influence the ARCM activities in GSD were identified. The authors also performed a client–vendor-based analysis to investigate the significance of the considered factors for different organisation types. Additionally, they compared the data collected from researchers and practitioners. The comparison results (r _s (20) = 0.076) revealed that there exists a positive correlation between the rankings in each data set. The investigated factors were mapped to ten project management book of knowledge areas, and the mapping results revealed that human resource management is the most significant knowledge area amongst the investigated factors. The findings of this study provide a robust framework to assist GSD firms in implementing ARCM activities.

Data warehouse quality can be determined during the initial phases of data warehouse development by quantifying the structural complexity of multidimensional models using metrics. The structural complexity of a multidimensional model is guided by its elements, types, and relationships among those elements. So far, most of the researchers have dealt with metrics based on various elements (facts, dimensions, dimensional hierarchies, and hierarchy levels) existing in these models. However, not much consideration is given to different types of dimensions based on hierarchy types and different relationships among those elements. Therefore, this work proposes a comprehensive complexity metric for measuring multidimensional model complexity by taking into account various elements, their types and the relationships among the elements at various levels of granularity in these models. The theoretical validation of the proposed metric using the property-based framework given by Briand et al. characterises it as a complexity measure. Furthermore, the empirical study, employing statistical techniques (correlation and multinomial regression), on 26 multidimensional models and 20 subjects proved that the authors’ proposed metric is strongly correlated with multidimensional model understandability. Hence, this metric can be considered as a good predictor for data warehouse multidimensional model understandability.

Software evolution is an important activity in the life cycle of a modern software system. In the process of software evolution, the repair of historical defects and the increasing demands may introduce new defects. Therefore, evolution-oriented defect prediction has attracted much attention of researchers in recent years. At present, some researchers have proposed the process metrics to describe the characteristics of software evolution. However, compared with the traditional software defect prediction methods, the research on evolution-oriented defect prediction is still inadequate. Based on the evolution data of object-oriented programs, this study presented two new process metrics from the defect rates of historical packages and the change degree of classes. To show the effectiveness of the proposed process metrics, the authors made comparisons with the code metrics and other process metrics. An empirical study was conducted on 33 versions of nine open-source projects. The results showed that adding the proposed process metrics could improve the performance of evolution-oriented defect prediction effectively.

This study examines the potential of using stage-wise process audit review and control (ARC) efforts in estimating overall effort and defects in a software project. Using archival data from 49 software projects that were based on the waterfall methodology, and obtained from a CMMI level 5 organisation, the authors found that higher ARC efforts at the requirement and build phases of a project were associated with an increase in overall project effort. Further, higher ARC efforts at the design and build phases were correlated with an increase in the number of defects delivered to the client at the end of the project. The predictive ability of the authors’ effort models was very high, with mean errors in the range of 3–4% of the overall project effort. They found that the mean ARC effort in their sample was 0.15 h/FP for the requirements stage, 0.31 h/FP for the design stage and 0.67 h/FP for the build stage. A project where the ARC effort for any of the three stages exceeded the respective benchmark, could be a candidate for deeper inspection to prevent excess effort or defects. Organisations can use their own data to develop similar benchmarks for their software project portfolios.

The rapidly increasing of sentiment analysis in social networks has lead business owners and decision makers to value opinion leaders who can influence people's impressions concerning certain business or commodity. Nevertheless, decision makers are being misled by inaccurate results due to the ignorance of perspectivism. Considering perspectivism, while computing text polarity, can help machines to reflect the human perceived sentiment within the content. This emphasises the need for integrating social behaviour (user's influence factor) with sentiment analysis (text polarity scores), providing a more pragmatic portrayal of how the writer's audience comprehend the message. In this study, a new model is proposed to intensify sentiment analysis process on Twitter. In the achievement of such, social network analysis is done using UCINET tool followed by artificial neural networks for ranking users. For sentiment classification, a hybrid approach is presented, where lexicon-based technique is combined with a fuzzy classification technique to handle language vagueness as well as for an inclusive analysis of tweets into seven classes; for the purpose of enhancing final results. The proposed model is practiced on data collected from Twitter. Results show a significant enhancement in tweets polarity scores represent more realistic sentiments.

There is a significant need to give careful consideration to the Capability Maturity Model Integration (CMMI) level 2 specific practices (i.e. SP 1.1 ‘understand requirements’ and SP 1.2 ‘obtain commitment to requirements’), especially in the context of small- and medium-sized software development organisations, in order to assist such organisations in effectively managing their requirements engineering processes. In this study, the authors propose an abstract-level model for each of these two specific practices as well as cover the initial evaluation of the models. In addition, necessary templates and checklists are also provided for each proposed model. The proposed models are based on a significant amount of research in software process improvement, CMMI and requirements engineering. The initial evaluation of the proposed models was executed using an expert panel review process. The results showed that the proposed models provide ease of learning and ease of use, provide stakeholder satisfaction and can be applied to small-and medium-sized software development organisations. It is important to highlight that this study contributes not only to the implementation of SP 1.1 and SP 1.2 of REQM process area in the context of small- and medium-sized software development organisations but also to the body of knowledge on REQM.