Using Automatic Static Analysis to Identify Technical Debt

The technical debt (TD) metaphor describes a tradeoff between short-term and long-term goals in software development. Developers, in such situations, accept compromises in one dimension (e.g. maintainability) to meet an urgent demand in another dimension (e.g. delivering a release on time). Since TD produces interests in terms of time spent to correct the code and accomplish quality goals, accumulation of TD in software systems is dangerous because it could lead to more difficult and expensive maintenance. The research presented in this paper is focused on the usage of automatic static analysis to identify Technical Debt at code level with respect to different quality dimensions. The methodological approach is that of Empirical Software Engineering and both past and current achieved results are presented, focusing on functionality, efficiency and maintainabilit

Research Findings on Empirical Evaluation of Requirements Specifications Approaches

Numerous software requirements specification (SRS) approaches have been proposed in software engineering. However, there has been little empirical evaluation of the use of these approaches in specific contexts. This paper describes the results of a mapping study, a key instrument of the evidence-based paradigm, in an effort to understand what aspects of SRS are evaluated, in which context, and by using which research method. On the basis of 46 identified and categorized primary studies, we found that understandability is the most commonly evaluated aspect of SRS, experiments are the most commonly used research method, and the academic environment is where most empirical evaluation takes place

Agile project and portfolio management: A systematic literature review

The application of Agile Methods (AM) in project management has shown great efficiency and remarkable results. However, these methods were born to be applied at team level and not at project portfolio level, where the complexity of managing multiple agile projects and teams leads to several challenges of resource management, priorities and governance model of the project portfolio. The systematic literature review performed in this research aims to identify which practices are the most appropriate to manage the challenges associated with Agile Portfolio Management (APM) and which is the most appropriate context.info:eu-repo/semantics/publishedVersio

The Importance of Using Empirical Evidence in Software Engineering

Experiments that are run with few experimental subjects are often considered not to be very reliable and deemed, as a result, to be useless with a view to generating new knowledge. This belief is not, however, entirely correct. Today we have tools, such as meta-analysis, that we can use to aggregate small-scale experiments and output results that are equivalent to experiments run on large samples that are therefore reliable. The application of meta-analysis can overcome some of the obstacles that we come up against when running software engineering experiments (such as, for example, the practitioner availability problem)

Candoia: A Platform and an Ecosystem for Building and Deploying Versatile Mining Software Repositories Tools

Research on mining software repositories (MSR) has shown great promise during the last decade in solving many challenging software engineering problems. There exists, however, a ‘valley of death’ between these significant innovations in the MSR research and their deployment in practice. The significant cost of converting a prototype to software; need to provide support for a wide variety of tools and technologies e.g. CVS, SVN, Git, Bugzilla, Jira, Issues, etc, to improve applicability; and the high cost of customizing tools to practitioner-specific settings are some key hurdles in transition to practice. We describe Candoia, a platform and an ecosystem that is aimed at bridging this valley of death between innovations in MSR research and their deployment in practice. We have implemented Candoia and provide facilities to build and publish MSR ideas as Candoia apps. Our evaluation demonstrates that Candoia drastically reduces the cost of converting an idea to an app, thus reducing the barrier to transitioning research findings into practice. We also see versatility, in Candoia app’s ability to work with a variety of tools and technologies that the platform supports. Finally, we find that customizing Candoia app to fit project-specific needs is often well within the grasp of developers

HISTORICAL ANALYSIS OF MESSAGE CONTENTS TO RECOMMEND ISSUES TO OPEN SOURCE SOFTWARE CONTRIBUTORS

Developers of distributed open source projects make use of issue tracker tools to coordinate their work. These tools store valuable information, maintaining a log of relevant decisions and bug solutions. Finding the appropriate issues to contribute can be hard, as the high volume of data increases contributors’ overhead. This paper shows the importance of the content of issue tracker discussions in an open source project to build a classifier to predict the participation of a contributor in an issue. To design this prediction model, we used two machine learning algorithms called Naïve Bayes and J48. We used data from the Apache Hadoop Commons project to evaluate the use of the algorithms. By applying machine learning algorithms to the ten most active contributors of this project, we achieved an average recall of 66.82% for Naïve Bayes and 53.02% using J48. We achieved 64.31% of precision and 90.27% of accuracy using J48. We also conducted an exploratory study with five contributors that took part in fewer issues and achieved 77.41% of precision, 48% of recall, and 98.84% accuracy using J48 algorithm. The results indicate that the content of comments in issues of open source projects is a relevant factor to recommend issues to contributors.Os desenvolvedores de projetos de software livre distribuídos utilizam ferramentas de acompanhamento de pendências para coordenar o seu trabalho. Essas ferramentas armazenam informações importantes, mantendo registro de decisões importantes e soluções para bugs. Decidir sobre que pendências são as mais adequadas para se contribuir pode ser difícil, uma vez que a elevada quantidade de dados aumenta a pressão sobre os desenvolvedores. Este artigo mostra a importância do conteúdo das discussões que ocorrem por meio da ferramenta de acompanhamento de pendências em um projeto de software livre para a construção de um classificador para predizer a participação de um colaborador na solução de um problema. Para projetar este modelo de predição, utilizamos dois algoritmos de aprendizagem de máquina: Naïve Bayes e J48. Utilizamos dados do projeto Apache Hadoop Commons para avaliar o uso dos algoritmos. Aplicando algoritmos de aprendizado de máquina aos dez desenvolvedores mais ativos no projeto, obtivemos uma média de recall de 66,82% para Naïve Bayes e 53,02% usando J48. Obtivemos 64,31% de precisão e 90,27% de acurácia usando o J48. Também realizamos um estudo exploratório com cinco desenvolvedores que participaram na solução de um volume menor de problemas , obtendo 77,41% de precisão, 48% de recall, e 98,84% de acurácia usando o algoritmo J48. Os resultados indicam que o conteúdo dos comentários em pendências/ problemas em projetos de software livre representam um fator relevante com base no qual recomendar pendências aos desenvolvedores que colaboram com o projeto

Fuzzy set and cache-based approach for bug triaging

Software bugs are inevitable and bug fixing is an essential and costly phase during software development. Such defects are often reported in bug reports which are stored in an issue tracking system, or bug repository. Such reports need to be assigned to the most appropriate developers who will eventually fix the issue/bug reported. This process is often called Bug Triaging. Manual bug triaging is a difficult, expensive, and lengthy process, since it needs the bug triager to manually read, analyze, and assign bug fixers for each newly reported bug. Triagers can become overwhelmed by the number of reports added to the repository. Time and efforts spent into triaging typically diverts valuable resources away from the improvement of the product to the managing of the development process. To assist triagers and improve the bug triaging efficiency and reduce its cost, this thesis proposes Bugzie, a novel approach for automatic bug triaging based on fuzzy set and cachebased modeling of the bug-fixing capability of developers. Our evaluation results on seven large-scale subject systems show that Bugzie achieves significantly higher levels of efficiency and correctness than existing state-of-the-art approaches. In these subject projects, Bugzie\u27s accuracy for top-1 and top-5 recommendations is higher than those of the second best approach from 4-15% and 6-31%, respectively as Bugzie\u27s top-1 and top-5 recommendation accuracy is generally in the range of 31-51% and 70-83%, respectively. Importantly, existing approaches take from hours to days (even almost a month) to finish training as well as predicting, while in Bugzie, training time is from tens of minutes to an hour

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used

La importancia del uso de la evidencia empírica en Ingeniería de Software

Un caso de estudio propuesto por la literatura en Ingeniería de Software Empírica describe a un Dr. Smith, que investiga sobre técnicas de testing en la universidad. Recientemente ha desarrollado una nueva técnica de inspección de código que, a priori, parece que mejora el desempeño de otras técnicas como por ejemplo la técnica basada en perspectivas. Por tal motivo, decide realizar un estudio empírico que le permita validar esta hipótesis. Para ello hace un llamado a estudiantes de los últimos años de la carrera de Ingeniería en Sistemas para que participen en el proyecto. Como resultado de la convocatoria consigue reclutar a 16 estudiantes, los cuales son entrenados 8 en la nueva técnica y 8 en la técnica basada en perspectivas. Con posterioridad, cada grupo aplica la técnica de inspección correspondiente al mismo programa, registrando el número de defectos encontrados.Presentado en el VII Workshop Ingeniería de Software (WIS

La importancia del uso de la evidencia empírica en Ingeniería de Software

Un caso de estudio propuesto por la literatura en Ingeniería de Software Empírica describe a un Dr. Smith, que investiga sobre técnicas de testing en la universidad. Recientemente ha desarrollado una nueva técnica de inspección de código que, a priori, parece que mejora el desempeño de otras técnicas como por ejemplo la técnica basada en perspectivas. Por tal motivo, decide realizar un estudio empírico que le permita validar esta hipótesis. Para ello hace un llamado a estudiantes de los últimos años de la carrera de Ingeniería en Sistemas para que participen en el proyecto. Como resultado de la convocatoria consigue reclutar a 16 estudiantes, los cuales son entrenados 8 en la nueva técnica y 8 en la técnica basada en perspectivas. Con posterioridad, cada grupo aplica la técnica de inspección correspondiente al mismo programa, registrando el número de defectos encontrados.Presentado en el VII Workshop Ingeniería de Software (WIS)Red de Universidades con Carreras en Informática (RedUNCI