The usefulness of mobile devices has increased greatly in recent years allowing users to perform more tasks in a mobile context. This increase in usefulness has come at the expense of the usability of these devices in some contexts. We conducted a small review of mobile usability models and found that usability is usually measured in terms of three attributes; effectiveness, efficiency and satisfaction. Other attributes, such as cognitive load, tend to be overlooked in the usability models that are most prominent despite their likely impact on the success or failure of an application. To remedy this we introduces the PACMAD (People At the Centre of Mobile Application Development) usability model which was designed to address the limitations of existing usability models when applied to mobile devices. PACMAD brings together significant attributes from different usability models in order to create a more comprehensive model. None of the attributes that it includes are new, but the existing prominent usability models ignore one or more of them. This could lead to an incomplete usability evaluation. We performed a literature search to compile a collection of studies that evaluate mobile applications and then evaluated the studies using our model.

Mobile app reviews are valuable repositories of ideas coming directly from app users. Such ideas span various topics, and in this paper we show that 23.3% of them represent feature requests, i.e. comments through which users either suggest new features for an app or express preferences for the re-design of already existing features of an app. One of the challenges app developers face when trying to make use of such feedback is the massive amount of available reviews. This makes it difficult to identify specific topics and recurring trends across reviews. Through this work, we aim to support such processes by designing MARA (Mobile App Review Analyzer), a prototype for automatic retrieval of mobile app feature requests from online reviews. The design of the prototype is a) informed by an investigation of the ways users express feature requests through reviews, b) developed around a set of pre-defined linguistic rules, and c) evaluated on a large sample of online reviews. The results of the evaluation were further analyzed using Latent Dirichlet Allocation for identifying common topics across feature requests, and the results of this analysis are reported in this paper.Index Terms-Online reviews, mobile apps, feature requests.

This paper describes the results of an investigation into a set of metrics for object-oriented design, called the MOOD metrics. The merits of each of the six MOOD metrics is discussed from a measurement theory viewpoint, taking into account the recognized object-oriented features which they were intended to measure: encapsulation, inheritance, coupling, and polymorphism. Empirical data, collected from three different application domains, is then analyzed using the MOOD metrics, to support this theoretical validation. Results show that (with appropriate changes to remove existing problematic discontinuities) the metrics could be used to provide an overall assessment of a software system, which may be helpful to managers of software development projects. However, further empirical studies are needed before these results can be generalized.

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Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step.Further Results and replication package:

The size of software project teams has been considered to be a driver of project productivity. Although there is a large literature on this, new publicly available software repositories allow us to empirically perform further research. In this paper we analyse the relationships between productivity, team size and other project variables using the International Software Benchmarking Standards Group (ISBSG) repository. To do so, we apply statistical and machine learning approaches to a preprocessed subset of the ISBSG repository to facilitate the study. The results show some expected correlations between productivity, effort and time as well as corroborating some other beliefs concerning team size and productivity. In addition, this study concludes that in order to apply statistical or data mining techniques to these type of repositories extensive preprocessing of the data needs to be performed due to ambiguities, wrongly recorded values, missing values, unbalanced datasets, etc. Such preprocessing is a difficult and error prone activity that would need further guidance and information that is not always provided in the repository.

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In this paper, we explore the content of online reviews of mobile applications to get a better understanding of the most recurring issues users report through reviews, and the way the price and the rating of an app influences the type and the amount of feedback users report. Results show that users tend to provide positive feedback, often associating it with requirements for additional features. Also, users tend to provide more feedback for the lower rated apps and the optimal price range was found to be between £2.25 and £3.50.