Natural user interface (NUI) is considered a recent topic in human–computer interaction (HCI) and provides innovative forms of interaction, which are performed through natural movements of the human body like gestures, voice, and gaze. In the software development process, usability and user eXperience (UX) evaluations are a relevant step, since they evaluate several aspects of the system, such as efficiency, effectiveness, user satisfaction, and immersion. Thus, the goal of the authors’ systematic mapping study (SMS) is to identify usability and UX evaluation technologies used by researchers and developers in software with NUIs. Their SMS selected 56 papers containing evaluation technologies for NUI. Overall, the authors identified 30 different usability and UX evaluation technologies for NUI. The analysis of these technologies reveals most of them are used to evaluate software in general, without considering the specificities of NUI. Besides, most technologies evaluate only one aspect, Usability or UX. In other words, these technologies do not consider Usability and UX together. For future work, they intend to develop an evaluation technology for NUIs that fills the gaps identified in their SMS and combining Usability and UX.

Dynamic software updating (DSU) is shifting gears to modify software systems without a halt. Even though extensive research has been conducted on DSU, it is necessary to synthesise and map the results of recent studies on DSU for prospective research highlights. This study aims to highlight the current state-of-the-art, to recognise trends, and to identify existing open issues in DSU. A systematic mapping study (SMS) was conducted with a set of six research questions. A total of 1066 papers published from 2005 to 2019 were recorded. After a filtering process, 112 primary studies were selected and inspected. This study highlights the current state-of-the-art of DSU including approaches, tools, models, and techniques. Also, this study outlines application domains, research type, and contributions type facets in DSU. In addition, this study demonstrates benchmarks, datasets, evaluation metrics, and existing open issues for future research in DSU. The results of this investigation can be used to highlight current state-of-the-art of DSU, to show trends of DSU, and to demonstrate existing open issues in DSU.

Requirement prioritisation and selection is an essential activity in modern-day large software development. Optimal prioritisation process is critical for successful implementation and release planning in a software development project. Requirement prioritisation becomes more challenging in projects having large sets of requirements and stakeholders, having diverse perspectives resulting in irrelevancy and ambiguity during features extraction. This study aims to improve requirement prioritisation process using text mining and clustering techniques for accurate extraction of features and requirement prioritisation in multi-stakeholder context. The proposed framework developed to avoid incompleteness in requirements and disagreement among development teams and stakeholders. Thus, the proposed framework compared with other requirement prioritisation techniques (i.e. Analytical Heretical Process, Commutative Voting and Wiegers) to highlight the significance of the proposed framework while conducting an experimental study. The results show that the proposed framework outperformed the traditional techniques and enhanced the prioritisation process with complete semantic information of extracted features and taking into account the diverse perspective of stakeholders.

Adaptive random testing (ART) achieves better failure-detection effectiveness than random testing due to its even spreading of test cases. ART by random partitioning (RP-ART) is a lightweight method, but its advantage over random testing is relatively low. Although iterative partition testing (IPT) method has good performance for detecting failures in a block pattern, it loses randomness during the test case generation. To overcome the shortcomings of the above two algorithms, a new algorithm named ART by flexible partitioning (FP-ART) is proposed. In the FP-ART, a set of random candidates is used to select an appropriate test case by considering their boundary distance. Accordingly, the corresponding sub-domain is also partitioned by the new test case. Based on this kind of flexible partitioning, the randomness of test case selection can be guaranteed and the spatial distribution of test cases is even more diverse. According to the results in simulation and empirical experiments, FP-ART demonstrates better failure-detection effectiveness than RP-ART and is more suitable to detect the failures in strip patterns than the IPT method. Meanwhile, its failure-detection ability is much stronger than that of fixed-size-candidate-set ART in the cases of a relatively high failure rate.

Code smells are indicators of inappropriate and possibly harmful design decisions that could lead to issues in the comprehensibility and maintainability of software systems. To avoid such quality complications, understanding the presence and prioritising the removal of code smells are required. This study presents a visualisation approach to help better understanding the evolutional characteristics of code smells presented in the different versions of the software system. The core of the visualisation approach is the metaphor of buildings and building blocks. An overall framework for detecting, categorising and visualising code smells is proposed. Three types of code smells were considered in this study. The considered code smells are God Class, Long Method and Type Checking. The applicability of the proposed approach is demonstrated by evaluating several versions of an open-source java software and visualising the detected code smells. Additionally, a pilot experimental study is conducted to empirically assure the usefulness of the proposed visualisations.