Proactive Empirical Assessment of New Language Feature Adoption via Automated Refactoring: The Case of Java 8 Default Methods

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations in the area. Here, we explore Java 8 default methods, which allow interfaces to contain (instance) method implementations. Default methods can ease interface evolution, make certain ubiquitous design patterns redundant, and improve both modularity and maintainability. A focus of this work is to discover, through a scientific approach and a novel technique, situations where developers found these constructs useful and where they did not, and the reasons for each. Although several studies center around assessing new language features, to the best of our knowledge, this kind of construct has not been previously considered. Despite their benefits, we found that developers did not adopt default methods in all situations. Our study consisted of submitting pull requests introducing the language feature to 19 real-world, open source Java projects without altering original program semantics. This novel assessment technique is proactive in that the adoption was driven by an automatic refactoring approach rather than waiting for developers to discover and integrate the feature themselves. In this way, we set forth best practices and patterns of using the language feature effectively earlier rather than later and are able to possibly guide (near) future language evolution. We foresee this technique to be useful in assessing other new language features, design patterns, and other programming idioms

On the Distribution of Test Smells in Open Source Android Applications: An Exploratory Study

The impact of bad programming practices, such as code smells, in production code has been the focus of numerous studies in software engineering. Like production code, unit tests are also affected by bad programming practices which can have a negative impact on the quality and maintenance of a software system. While several studies addressed code and test smells in desktop applications, there is little knowledge of test smells in the context of mobile applications. In this study, we extend the existing catalog of test smells by identifying and defining new smells and survey over 40 developers who confirm that our proposed smells are bad programming practices in test suites. Additionally, we perform an empirical study on the occurrences and distribution of the proposed smells on 656 open-source Android apps. Our findings show a widespread occurrence of test smells in apps. We also show that apps tend to exhibit test smells early in their lifetime with different degrees of co-occurrences on different smell types. This empirical study demonstrates that test smells can be used as an indicator for necessary preventive software maintenance for test suites

Empirical assessment of the adoption, use, and effects of pair programming

Developing large software systems requires team work, which in turn calls for lots of communication within the team. However, programming is typically conducted alone by individual software developers. Pair programming, where two persons actively collaborate in the implementation of a single task, is an alternative way of developing software. It has been proposed as a means to increasing software quality, knowledge transfer and learning, among other things. This research studied the adoption, use, and effects of pair programming through a literature study and three empirical studies. The literature study was a systematic mapping study of the previous pair programming research in the industry. The empirical studies consisted of two long industry case studies and an experiment where project teams consisting of experienced students conducted a moderately large software development project. The systematic mapping study analyzed the content of 154 papers. It identified industrially relevant aspects of pair programming and organized them as a pair programming framework containing additional and more detailed aspects of pair programming over the previously published frameworks. The framework grouped all the identified aspects under eighteen factors of pair programming, for which their state of research was analyzed. The analysis showed that of many factors, only a few or no studies had been conducted using rigorous research approaches and data collection methods. The adoption and use of pair programming were analyzed in the two case studies. In the larger, more established organization, there were issues with adoption, related to both infrastructure and organizing of pair programming. A separate pair programming room was a successful solution to the infrastructural issues. However, lack of time for pair programming due to insufficient organizing of its use, remained an issue at the end of the study. The effects of pair programming on software quality and developers' knowledge were positive in all three empirical studies, but the development effort for individual tasks increased. The increase in effort occurred mainly when using pair programming for simple tasks or during the beginning of a project, when the developers were learning pair programming and getting to know one another

Places of Poverty and Powerlessness: INGOs working ‘at home’

The search for transformatory development practice, distanced from colonial binaries and representations, has been the focus of decades of scholarship. Recent research suggests that INGOs are central in this regard, whether in their governance, fundraising, advocacy, knowledge-management, engagement with others or their approach to programme design. This paper progresses these debates by providing empirical evidence of the value of domestic programming in this ‘project’. Drawing on three case studies, the paper finds evidence of INGOs’ search for a programme strategy, which moves minimising the violence of ‘othering’ from theory to practice

Knowledge discovery in biological databases : a neural network approach

Knowledge discovery, in databases, also known as data mining, is aimed to find significant information from a set of data. The knowledge to be mined from the dataset may refer to patterns, association rules, classification and clustering rules, and so forth. In this dissertation, we present a neural network approach to finding knowledge in biological databases. Specifically, we propose new methods to process biological sequences in two case studies: the classification of protein sequences and the prediction of E. Coli promoters in DNA sequences. Our proposed methods, based oil neural network architectures combine techniques ranging from Bayesian inference, coding theory, feature selection, dimensionality reduction, to dynamic programming and machine learning algorithms. Empirical studies show that the proposed methods outperform previously published methods and have excellent performance on the latest dataset. We have implemented the proposed algorithms into an infrastructure, called Genome Mining, developed for biosequence classification and recognition