Dificultades de los “recién llegados” a proyectos software en ejecución

No es poco frecuente que, en los proyectos software, sea necesario incorporar nuevos desarrolladores en una etapa avanzada de su ejecución. En estas circunstancias, estos “recién llegados” enfrentan varias dificultades y desafíos que les impiden comenzar rápidamente a contribuir, con sus conocimientos y experiencia previos, a la marcha del proyecto. Este artículo reporta los resultados de un estudio exploratorio-descriptivo dirigido a identificar las dificultades a las que se enfrentan los nuevos miembros del equipo de proyecto al unirse a un proyecto en ejecución, así como identificar las acciones que usualmente se adoptan para mitigar estos problemas y dificultades. El estudio revela que la escasa o nula documentación y la necesidad de conocer el producto en construcción son las principales dificultades, mientras que la asignación de un referente y la provisión de capacitación se mencionan como las principales acciones que las organizaciones suelen tomar para mitigar esos problemas.XIV Workshop de Ingeniería de Software (WIS).Red de Universidades con Carreras en Informática (RedUNCI

Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software

Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects' quality and reliability, and the execution of "safe and uninformative" test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings.Comment: arXiv admin note: substantial text overlap with arXiv:2111.0466

“Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub

Context: Addressing user requests in the form of bug reports and Github issues represents a crucial task of any successful software project. However, user-submitted issue reports tend to widely differ in their quality, and developers spend a considerable amount of time handling them. Objective: By collecting a dataset of around 6,000 issues of 279 GitHub projects, we observe that developers take significant time (i.e., about five months, on average) before labeling an issue as a wontfix. For this reason, in this paper, we empirically investigate the nature of wontfix issues and methods to facilitate issue management process. Method: We first manually analyze a sample of 667 wontfix issues, extracted from heterogeneous projects, investigating the common reasons behind a “wontfix decision”, the main characteristics of wontfix issues and the potential factors that could be connected with the time to close them. Furthermore, we experiment with approaches enabling the prediction of wontfix issues by analyzing the titles and descriptions of reported issues when submitted. Results and conclusion: Our investigation sheds some light on the wontfix issues’ characteristics, as well as the potential factors that may affect the time required to make a “wontfix decision”. Our results also demonstrate that it is possible to perform prediction of wontfix issues with high average values of precision, recall, and F-measure (90%-93%)

Práticas de Recursos Humanos: o Caso do Acolhimento Inovador na hes - sistemas informáticos

A área de Gestão de Recursos Humanos (GRH) tem-se mostrado cada vez mais importante no meio organizacional, apresentando-se com um carater fundamental na estratégia da empresa. Atualmente, a área da GRH, tem tido um vasto reconhecimento na produtividade dos colaboradores, através das suas inúmeras práticas. As inovações verificadas nos dias de hoje, visam, essencialmente, a produtividade e os processos de GRH não são exceção quer seja para a atração e retenção do talento ou para a atualização de processos anteriormente burocráticos. Idealizar um processo de acolhimento com caráter inovador possibilita o acompanhamento e o relacionamento entre colegas e chefia . Além disso, permite ao novo colaborador perceber a cultura organizacional, quais as suas tarefas e os seus objetivos, de forma a manter-se empenhado e focado na sua tarefa tornando-se um colaborador efetivo da empresa. Consequentemente, este trabalho reflete o estágio curricular realizado na hes-sistemas informáticos, no âmbito do Mestrado de Gestão. O estágio foi desenvolvido no Departamento de Recursos Humanos da organização hes-sistemas informáticos, com o objetivo de idealizar e implementar o Descritivo e Analise de Funções, melhorar o processo de Recrutamento e Seleção, criação de um processo de Acolhimento Inovador, Avaliação de Desempenho, apoio nas tarefas administrativas e o registo de um conjunto de ideias para a melhoria continua da empresa

Test smells 20 years later : detectability, validity, and reliability

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Test smells aim to capture design issues in test code that reduces its maintainability. These have been extensively studied and generally found quite prevalent in both human-written and automatically generated test-cases. However, most evidence of prevalence is based on specific static detection rules. Although those are based on the original, conceptual definitions of the various test smells, recent empirical studies indicate that developers perceive warnings raised by detection tools as overly strict and non-representative of the maintainability and quality of test suites. This leads us to re-assess test smell detection tools’ detection accuracy and investigate the prevalence and detectability of test smells more broadly. Specifically, we construct a hand-annotated dataset spanning hundreds of test suites both written by developers and generated by two test generation tools (EvoSuite and JTExpert) and performed a multistage, cross-validated manual analysis to identify the presence of six types of test smells in these. We then use this manual labeling to benchmark the performance and external validity of two test smell detection tools – one widely used in prior work and one recently introduced with the express goal to match developer perceptions of test smells. Our results primarily show that the current vocabulary of test smells is highly mismatched to real concerns: multiple smells were ubiquitous on developer-written tests but virtually never correlated with semantic or maintainability flaws; machine-generated tests actually often scored better, but in reality, suffered from a host of problems not wellcaptured by current test smells. Current test smell detection strategies poorly characterized the issues in these automatically generated test suites; in particular, the older tool’s detection strategies misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice, (ii) more accurate detection strategies to be evaluated primarily in industrial contexts

Machine learning-based test selection for simulation-based testing of self-driving cars software

Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than eld operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing con dence in test subjects' quality and reliability, and the execution of \safe and uninformative" test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI

Supporting newcomers in software development projects

The recent and fast expansion of OSS (Open-source software) communities has fostered research on how open source projects evolve and how their communities interact. Several research studies show that the inflow of new developers plays an important role in the longevity and the success of OSS projects. Beside that they also discovered that an high percentage of newcomers tend to leave the project because of the socio-technical barriers they meet when they join the project. However, such research effort did not generate yet concrete results in support retention and training of project newcomers. In this thesis dissertation we investigated problems arising when newcomers join software projects, and possible solutions to support them. Specifically, we studied (i) how newcomers behave during development activities and how they interact with others developers with the aim at (ii) developing tools and/or techniques for supporting them during the integration in the development team. Thus, among the various recommenders, we defined (i) a tool able to suggest appropriate mentors to newcomers during the training stage; then, with the aim at supporting newcomers during program comprehension we defined other two recommenders: a tool that (ii) generates high quality source code summaries and another tool able to (iii) provide descriptions of specific source code elements. For future work, we plan to improve the proposed recommenders and to integrate other kind of recommenders to better support newcomers in OSS projects