Fabio Kon

Henrique Lemos Ribeiro, Higor Amario de Souza, Roberto Paulo de Andrioli Araujo et al.

Background: Debugging is a key task during the software development cycle. Spectrum-based Fault Localization (SFL) is a promising technique to improve and automate debugging. SFL techniques use control-flow spectra to pinpoint the most suspicious program elements. However, data-flow spectra provide more detailed information about the program execution, which may be useful for fault localization. Aims: We evaluate the effectiveness and efficiency of ten SFL ranking metrics using data-flow spectra. Method: We compare the performance of data- and control-flow spectra for SFL using 163 faults from 5 real-world open source programs, which contain from 468 to 4130 test cases. The data- and control-flow spectra types used in our evaluation are definition-use associations (DUAs) and lines, respectively. Results: Using data-flow spectra, up to 50% more faults are ranked in the top-15 positions compared to control-flow spectra. Also, most SFL ranking metrics present better effectiveness using data-flow to inspect up to the top-40 positions. The execution cost of data-flow spectra is higher than control-flow, taking from 22 seconds to less than 9 minutes. Data-flow has an average overhead of 353% for all programs, while the average overhead for control-flow is of 102%. Conclusions: The results suggest that SFL techniques can benefit from using data-flow spectra to classify faults in better positions, which may lead developers to inspect less code to find bugs. The execution cost to gather data-flow is higher compared to control-flow, but it is not prohibitive. Moreover, data-flow spectra also provide information about suspicious variables for fault localization, which may improve the developers' performance using SFL.

Isaque Alves, Fabio Kon, Jessica Diaz et al.

Software Engineering often adapts theory-building frameworks from the social sciences to address socio-technical complexity. The key phases of the theory-building process are conceptual development, operationalization, testing, and application. Operationalization translates abstract concepts into measurable elements for empirical validation. This phase is essential for delivering the practical utility required by an applied science like Software Engineering. We propose a systematic procedure for the operationalization phase that bridges the gap between abstract concepts and empirical validation, ensuring the resulting theory is both rigorous and practically useful. We extend the operationalization framework proposed by Sjøberg et al. and formulate non-causal hypotheses following Dubin's approach. Our procedure defines variables, selects indicators, and systematically derives hypotheses. We present a replicable, evidence-based methodological guideline that preserves a clear chain of evidence and supports practical validation. We illustrate the procedure using the DevOps Team Taxonomies Theory. This guideline provides a transparent chain of evidence from theory to testable elements, empowering researchers to ground theoretical advancements in empirical evidence and deliver actionable insights for practitioners.

Leonardo Leite, Gustavo Pinto, Fabio Kon et al.

Context: To accelerate time-to-market and improve customer satisfaction, software-producing organizations have adopted continuous delivery practices, impacting the relations between development and infrastructure professionals. Yet, no substantial literature has substantially tackled how the software industry structures the organization of development and infrastructure teams. Objective: In this study, we investigate how software-producing organizations structure their development and infrastructure teams, specifically how is the division of labor among these groups and how they interact. Method: After brainstorming with 7 DevOps experts to better formulate our research and procedures, we collected and analyzed data from 37 semi-structured interviews with IT professionals, following Grounded Theory guidelines. Results: After a careful analysis, we identified four common organizational structures: (1) siloed departments, (2) classical DevOps, (3) cross-functional teams, and (4) platform teams. We also observed that some companies are transitioning between these structures. Conclusion: The main contribution of this study is a theory in the form of a taxonomy that organizes the found structures along with their properties. This theory could guide researchers and practitioners to think about how to better structure development and infrastructure professionals in software-producing organizations.

Leonardo Leite, Carla Rocha, Fabio Kon et al.

DevOps is a collaborative and multidisciplinary organizational effort to automate continuous delivery of new software updates while guaranteeing their correctness and reliability. The present survey investigates and discusses DevOps challenges from the perspective of engineers, managers, and researchers. We review the literature and develop a DevOps conceptual map, correlating the DevOps automation tools with these concepts. We then discuss their practical implications for engineers, managers, and researchers. Finally, we critically explore some of the most relevant DevOps challenges reported by the literature.

Eduardo Felipe Zambom Santana, Ana Paula Chaves, Marco Aurelio Gerosa et al.

Making cities smarter help improve city services and increase citizens' quality of life. Information and communication technologies (ICT) are fundamental for progressing towards smarter city environments. Smart City software platforms potentially support the development and integration of Smart City applications. However, the ICT community must overcome current significant technological and scientific challenges before these platforms can be widely used. This paper surveys the state-of-the-art in software platforms for Smart Cities. We analyzed 23 projects with respect to the most used enabling technologies, as well as functional and non-functional requirements, classifying them into four categories: Cyber-Physical Systems, Internet of Things, Big Data, and Cloud Computing. Based on these results, we derived a reference architecture to guide the development of next-generation software platforms for Smart Cities. Finally, we enumerated the most frequently cited open research challenges, and discussed future opportunities. This survey gives important references for helping application developers, city managers, system operators, end-users, and Smart City researchers to make project, investment, and research decisions.

Higor A. de Souza, Marcos L. Chaim, Fabio Kon

Despite being one of the most basic tasks in software development, debugging is still performed in a mostly manual way, leading to high cost and low performance. To address this problem, researchers have studied promising approaches, such as Spectrum-based Fault Localization (SFL) techniques, which pinpoint program elements more likely to contain faults. This survey discusses the state-of-the-art of SFL, including the different techniques that have been proposed, the type and number of faults they address, the types of spectra they use, the programs they utilize in their validation, the testing data that support them, and their use at industrial settings. Notwithstanding the advances, there are still challenges for the industry to adopt these techniques, which we analyze in this paper. SFL techniques should propose new ways to generate reduced sets of suspicious entities, combine different spectra to fine-tune the fault localization ability, use strategies to collect fine-grained coverage levels from suspicious coarser levels for balancing execution costs and output precision, and propose new techniques to cope with multiple-fault programs. Moreover, additional user studies are needed to understand better how SFL techniques can be used in practice. We conclude by presenting a concept map about topics and challenges for future research in SFL.