The impact of design patterns on quality attributes has been extensively evaluated in studies with different perspectives, objectives, metrics, and quality attributes, leading to contradictive and hard to compare results. The authors’ objective is to explain these results by considering confounding factors, practices, metrics, or implementation issues that affect quality. Furthermore, there is a lack of research that connects design patterns evaluations to patterns development studies. Accordingly, they also aim at providing an initiate on how patterns structure and implementation can be improved, to promote software quality. To achieve their goals, conducted a systematic literature review by searching the literature for related studies. The study covers the period between years 2000 and 2018. They identified 804 candidate papers. After applying inclusion and exclusion criteria, they were left with 50 primary studies. Their results show that documentation of patterns, size of pattern classes, and the scattering degree of patterns have clear impact on quality. In case studies, researchers used different metrics applied to different modules. Controlled experiments have major design differences. Reaching consensuses on the effect of patterns requires considering influencing factors, using unified metrics, and an agreement on what modules to measure. Studying how to improve patterns modularity is recommended for future research.

The low performance of software projects generally arises from erroneous, omitted, misinterpreted, or conflicting requirements. To produce better quality specifications, the practise of requirements reuse through requirement patterns has been widely debated in the secondary literature. However, a tertiary study that provides an overview of secondary studies on the state of the art and the practise of requirement patterns does not exist. This study describes a study of secondary literature on requirement patterns under a perspective on research and practise. The identification and selection methods of secondary studies include automatic search on five sources, inclusion, and exclusion criteria, the snowballing technique, and the quality assessment of those studies. Four secondary studies are considered relevant according to the purpose of this research from a 26-distinct-study group. The authors’ contribution is two-fold: the tertiary study itself and a preliminary research agenda dealing with state of the art and practise on requirement patterns.

Cross-platform apps stand out by their ability to run in various operating systems (OSs), such as Android, iOS, and Windows. Such apps are developed using popular frameworks for cross-platform app development such as Apache Cordova, Xamarin, and React Native. However, the mechanisms to automate their tests are not cross-platform and do not support multiple configurations. Hence, different test scripts have to be coded for each platform, yet there is no guarantee they will work in different configurations varying, e.g. platform, OS version, and hardware available. This study proposes mechanisms to produce automated tests for cross-platform mobile apps. In order to set up the tests to execute in multiple configurations, the authors’ approach adopts two reference devices: one running Android and other iOS. As both platforms have their own user interface (UI) XML representation, they also investigated six individual expression types and two combined strategies to locate UI elements. They have developed a prototype tool called cross-platform app test script recorder (x-PATeSCO) to support the proposed approach, as well as the eight locating strategies considered. They evaluated the approach with nine cross-platform mobile apps, comparing the locating strategies in six real devices.

To support the iterative development process of Apps, the goal model is not only established to describe the requirements at the early stage but also used for identifying the updating strategy in every iteration. In this process, reviews from users provide valuable information for developers to analyse the model with users sentiments. In this study, the authors combine the goal model with reviews for supporting the evolution of Apps. First, the authors introduce the reviews into the goal model as a new factor by comparing keywords. Second, the users sentiments in reviews are mined, and two kinds of information are gained by analysing the model to help developers make decisions on which goals to be improved in next version: one kind of information is about users sentiments on the goals to evaluate whether users like them; another kind is the impact of updating one goal to others. To validate the proposed approach, they conducted experiments and a survey based on the Apps in Google Play. The results show that the proposed approach can establish relationships between goals and reviews reasonably and further provide useful information for optimising the evolution strategy of the App.