Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide critical insights for the further development of opinion mining techniques in the SE domain

On the relation between architectural smells and source code changes

Although architectural smells are one of the most studied type of architectural technical debt, their impact on maintenance effort has not been thoroughly investigated. Studying this impact would help to understand how much technical debt interest is being paid due to the existence of architecture smells and how this interest can be calculated. This work is a first attempt to address this issue by investigating the relation between architecture smells and source code changes. Specifically, we study whether the frequency and size of changes are correlated with the presence of a selected set of architectural smells. We detect architectural smells using the Arcan tool, which detects architectural smells by building a dependency graph of the system analyzed and then looking for the typical structures of the architectural smells. The findings, based on a case study of 31 open-source Java systems, show that 87% of the analyzed commits present more changes in artifacts with at least one smell, and the likelihood of changing increases with the number of smells. Moreover, there is also evidence to confirm that change frequency increases after the introduction of a smell and that the size of changes is also larger in smelly artifacts. These findings hold true especially in Medium–Large and Large artifacts

Playing Planning Poker in Crowds: Human Computation of Software Effort Estimates

Reliable cost effective effort estimation remains a considerable challenge for software projects. Recent work has demonstrated that the popular Planning Poker practice can produce reliable estimates when undertaken within a software team of knowledgeable domain experts. However, the process depends on the availability of experts and can be time-consuming to perform, making it impractical for large scale or open source projects that may curate many thousands of outstanding tasks. This paper reports on a full study to investigate the feasibility of using crowd workers supplied with limited information about a task to provide comparably accurate estimates using Planning Poker. We describe the design of a Crowd Planning Poker (CPP) process implemented on Amazon Mechanical Turk and the results of a substantial set of trials, involving more than 5000 crowd workers and 39 diverse software tasks. Our results show that a carefully organised and selected crowd of workers can produce effort estimates that are of similar accuracy to those of a single expert

AN EYE TRACKING REPLICATION STUDY OF A RANDOMIZED CONTROLLED TRIAL ON THE EFFECTS OF EMBEDDED COMPUTER LANGUAGE SWITCHING

The use of multiple programming languages (polyglot programming) during software development is common practice in modern software development. However, not much is known about how the use of these different languages affects developer productivity. The study presented in this thesis replicates a randomized controlled trial that investigates the use of multiple languages in the context of database programming tasks. Participants in our study were given coding tasks written in Java and one of three SQL-like embedded languages: plain SQL in strings, Java methods only, a hybrid embedded language that was more similar to Java. In addition to recording the online questionnaire responses and the participants\u27 solutions to the tasks, the participants\u27 eye movements were also recorded using an eye tracker. Eye tracking as a method for software development studies has grown in recent years and allows for finer-grain information about how developers complete programming tasks. Eye tracking data was collected from 31 participants (from both academia and industry) for each of the six programming tasks they completed. Unlike the original study, we were unable to find a significant effect on productivity due to the language used or whether they were a native English speaker. However, we did find the same effect of participant experience on programming productivity which indicates that more experienced programmers are able to complete polyglot programming tasks in a more efficient manner. We also found that all participants looked at the sample code the same percentage of the time for a given task regardless of their experience or language variant they were given. The top level navigation behavior also remained largely unchanged across experience or language variants. We found that professionals performed more transitions in the code between the Java code and method parameters than their novice counterparts. Overall, we found that the level of polyglot programming did not have as significant of an effect as the task itself. The high-level strategy that participants employed appeared similar regardless of language variant they were given. Adviser: Bonita Shari

On the real world practice of Behaviour Driven Development

Surveys of industry practice over the last decade suggest that Behaviour Driven Development is a popular Agile practice. For example, 19% of respondents to the 14th State of Agile annual survey reported using BDD, placing it in the top 13 practices reported. As well as potential benefits, the adoption of BDD necessarily involves an additional cost of writing and maintaining Gherkin features and scenarios, and (if used for acceptance testing,) the associated step functions. Yet there is a lack of published literature exploring how BDD is used in practice and the challenges experienced by real world software development efforts. This gap is significant because without understanding current real world practice, it is hard to identify opportunities to address and mitigate challenges. In order to address this research gap concerning the challenges of using BDD, this thesis reports on a research project which explored: (a) the challenges of applying agile and undertaking requirements engineering in a real world context; (b) the challenges of applying BDD specifically and (c) the application of BDD in open-source projects to understand challenges in this different context. For this purpose, we progressively conducted two case studies, two series of interviews, four iterations of action research, and an empirical study. The first case study was conducted in an avionics company to discover the challenges of using an agile process in a large scale safety critical project environment. Since requirements management was found to be one of the biggest challenges during the case study, we decided to investigate BDD because of its reputation for requirements management. The second case study was conducted in the company with an aim to discover the challenges of using BDD in real life. The case study was complemented with an empirical study of the practice of BDD in open source projects, taking a study sample from the GitHub open source collaboration site. As a result of this Ph.D research, we were able to discover: (i) challenges of using an agile process in a large scale safety-critical organisation, (ii) current state of BDD in practice, (iii) technical limitations of Gherkin (i.e., the language for writing requirements in BDD), (iv) challenges of using BDD in a real project, (v) bad smells in the Gherkin specifications of open source projects on GitHub. We also presented a brief comparison between the theoretical description of BDD and BDD in practice. This research, therefore, presents the results of lessons learned from BDD in practice, and serves as a guide for software practitioners planning on using BDD in their projects