Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems

[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated.Comment: 10 pages, 8 figures, accepted for the 39th International Conference on Software Engineering (ICSE'17

Um estudo de caso industrial investigando o uso de dados de diferentes organizações para apoiar análise causal de defeitos

[Contexto] A Análise Causal de Defeitos (Defect Causal Analysis - DCA) representa uma prática eficiente para melhorar os processos de software. Enquanto conhecimento sobre as relações entre causa-efeito em defeitos de software pode ser útil para apoiar DCA, o levantamento de dados de causa-efeito pode exigir um significante esforço e tempo. [Objetivo] Avaliar em um estudo de caso industrial uma nova abordagem de DCA, que utiliza dados de outras empresas para apoiar a aplicação de DCA. [Método] Utilizamos dados coletados sobre causas de problemas de engenharia de requisitos de 74 empresas de tecnologia brasileiras para construir uma rede bayesiana. A abordagem de DCA proposta utiliza a inferência diagnóstica da rede bayesiana para dar suporte às sessões de DCA. Este trabalho diz respeito ao terceiro passo na aplicação de um modelo de transferência de tecnologia que envolveu conduzir três avaliações consecutivas: (i) na academia, (ii) com representantes da indústria, e (iii) em um estudo de caso industrial na Banco Nacional de Desenvolvimento Econômico e Social (BNDES). [Resultados] Recebemos feedback positivo em todas as três avaliações e a abordagem foi considerada útil para determinar as causas dos defeitos de software. [Conclusões] Nossos resultados reforçam a confiança de que a abordagem de DCA proposta, com o apoio de dados de outras empresas, é promissora e deve ser investigada mais a fundo[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge about cause-effect relationships concerning defects can be useful to support DCA, collecting cause-effect data may require significant time and effort. [Goal] Evaluate a new DCA approach, that uses cross-company data to support the application of DCA. [Method] We used cross-company data on causes of requirements engineering problems collected from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. This study concerns the third step in the application of a technology transfer model involved conducting three consecutive evaluations: (i) in academia, (ii) with industry representatives, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three assessments and the approach was considered helpful to determine the main causes of software defects. [Conclusions] Our results reinforce our confidence that the proposed DCA approach, supported by cross-company data, is promising and should be further investigate

Stepping into the Void: Lessons Learned from Civil Society Organizations during COVID-19 in Rio de Janeiro

Brazil experienced some of the highest rates of COVID-19 globally. This was complicated by the fact that 35 million of its citizens have limited access to water, a primary resource necessary to stem the spread of infectious diseases. In many cases, civil society organizations (CSOs) stepped into this void left by responsible authorities. This paper explores how CSOs in Rio de Janeiro helped populations struggling with access to water, sanitation, and hygiene (WASH) during the pandemic, and what coping strategies are transferable to similar contexts. In-depth interviews (n = 15) were conducted with CSO representatives in the metropolitan region of Rio de Janeiro. Thematic analysis of the interviews revealed that COVID-19 exacerbated pre-existing social inequities among vulnerable populations, undermining their ability to protect their health. CSOs provided emergency relief aid but faced the counterproductive actions of public authorities who promoted a narrative that diminished the risks of COVID-19 and the importance of non-pharmacological interventions. CSOs fought this narrative by promoting sensitization among vulnerable populations and partnering with other stakeholders in networks of solidarity, playing a vital role in the distribution of health-promoting services. These strategies are transferrable to other contexts where state narratives oppose public health understandings, particularly for extremely vulnerable populations