Introduction : Digital products increasingly reshape industries, influencing human behavior and decision-making. However, the software development teams developing these systems often lack diversity, which may lead to designs that overlook the needs, equal treatment or safety of diverse user groups. These risks highlight the need for fostering diversity and inclusion in software development to create safer, more equitable technology. Method : This research is based on insights from an academic meeting in June 2023 involving 23 software engineering researchers and practitioners. We used the collaborative discussion method 1-2-4-ALL as a systematic research approach and identified six themes around the theme “challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI).” We identified benefits, harms, and future research directions for the four main themes. Then, we discuss the remaining two themes, AI & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes. Results : This research explores the key challenges and research opportunities for promoting SDDI, providing a roadmap to guide both researchers and practitioners. We underline that research around SDDI requires a constant focus on maximizing benefits while minimizing harms, especially to vulnerable groups. As a research community, we must strike this balance in a responsible way.

The Technovation Girls program (TG) is an after-school program, where teams of female school students follow a software ideation process to address one of the UN's Sustainable Development goals. Concretely, they prototype a mobile application which they later pitch to judges. The TG curriculum gives students the tools they need to carry out this challenge, with activities focusing on entrepreneurship, product ideation and pitching, as well as coding. Previous evaluations of this program have focused on student and mentor perceptions and attitudes, collected through surveys, observation, focus groups and interviews. In this paper, we evaluate the effect that the coding part of the TG curriculum has on students' computational thinking skills, specifically in Chile, where the TG curriculum was expanded to a year to account for differences in the school curriculum. Also, in this experience, the TG program was carried out during class hours, and at both single- and mixed-gender vocational high schools. We applied an existing computational thinking test to the 2020 cohort (approx. 1000 students), before and after participating in the program, analyzing the results by school, gender and age. We found a slight improvement in test scores, with a more positive effect in male students.

A microprint is a visualization that condenses the contents of a text file into a small space. This is done by using colored scaled -down characters or pixels to represent each character of the text. The colors aim to highlight information relevant to the user. We have created μPrintGen, a framework for creating custom microprints by associating colors to rules that specify what is included or excluded from the line, with plain text or regular expressions. At the same time, the user can customize the look and structure of the created microprint for ease of use. We also created$\mu$PrintVis, a website for viewing the created microprints that supports various navigation features, like zoom and search. Feedback from the Rust and Julia open-source communities shows that μPrintGen lets users navigate through large log files, while easily highlighting reported errors. Two experts agree on the benefits of$\mu$PrintGen, compared to pure textual-based searching features commonly offered by text editors. Video URL: https://youtu.beIlJIZHLQdXiO

Chile is a medium-sized developing country, with approximately 3,500,000 school-aged children. Computational thinking (CT) has been recognized as a tool for developing XXI century skills, but Chile has a large population of in-service teachers that do not know what CT is, limiting its application in classrooms. In response, the government has enacted a training program for in-service primary school teachers, focusing on changing their perceptions about CT, and showing them how it can be used in classroom. This training program was designed for grades 1-6 and piloted in the capital region during 2018. The program initially focused on coding activities, but was then redesigned to show how CT can be used to help meet the learning objectives of different subjects in the curriculum. The program was then scaled up nationwide. In this experience report, we discuss the outcomes of this program. The training program scaled as intended and we identified positive changes in teacher perceptions about CT, especially among female participants. We conclude with the lessons we learned from this experience.

Parents that homeschool their children ignore certain topics when they lack mastery or interest in them. Homeschool groups try to address this issue, cooperatively educating their children. We were contacted by such a group that wanted to introduce their children to computational thinking (CT). These children, aged 7-11, have showed an interest in technology, and use online educational resources. None of the parents felt capable of tutoring the group about CT. They also worried about losing control about how their children interact with technology. We report an intervention over 9 months to introduce eleven young homeschoolers to CT in a suburban environment, describing the impact on parent and children attitudes towards technology and CT. We conclude with three lessons: 1)~science-related activities should be used to introduce CT among homeschoolers, 2) "success'' is establishing a meaningful relationship with a homeschool group, and 3) activities designed for school children need to be adapted to the homeschooling context.

In Latin America, computational thinking workshops are mostly developed in urban areas, charging participation fees. And although teachers are increasingly being expected to include technology in their classrooms, computational thinking and programming are not mandatory topics in teacher training programs. This hinders the development of digital skills among Latino students, and we expect that the digital gap between urban and rural populations will expand over time, especially in socio-economically vulnerable populations. Believing that teachers can be agents of change, we designed a 12 hour workshop to train the K-8 teaching staff in a rural and vulnerable school. The goal of this experience was to help these teachers develop basic computational thinking skills and devise new ways to incorporate what they learned in their classrooms. In this paper, we report our experience facilitating this workshop, and analyze the teacher perceptions before and after the intervention. Teacher attitudes changed drastically during the execution of the workshop, and they were able to come up with creative ways of incorporating computational thinking activities into their subjects. The reported experience can be used as input to develop public policies with respect to how computational thinking should be introduced in rural and vulnerable environments.

Advancing computational thinking in elementary education has been rapidly gaining attention due to the prospective of developing 21st century skills. However, interventions in this domain risk failure if they do not explicitly address the particular socio-cultural traits of the deployment scenario. This is the case in most countries of Latin America, where computing has not reached a sustainable penetration in K-12 education. In order to bridge this gap, we designed a one-week workshop for advancing computational thinking targeted to 10-12 years old Chilean students with no prior experience in programming. This paper describes our intervention and presents the results of a qualitative study analyzing positive and negative aspects of the experience. Although most participants effectively acquired basic programming skills by the end of the intervention, we also identified several inaccurate views on computing and computer scientists. For instance, computing was mostly perceived as a set of informal experiences rather than a way for enabling creation, automation, and work. The word "hacking" appears to be used as a metaphor for more technical terms, such as "programming" or "algorithm". Finally, negative stereotypical views of computer scientists resulting from the intervention were not as frequent as initial perceptions. These results provide fresh evidence on how to design, adapt, and evaluate computational thinking interventions targeted to K-6 students in Latin America.

The action of teaching reinforces one's learning but requires some external quality control when done by nonprofessionals (e.g., a professor supervising teaching assistants). This quality control is costly and has limited the adoption of peer teaching in schools. Our solution to this problem is to ask students to create pedagogical material that will then be evaluated by the students themselves, where they evaluate a mix of new and already rated materials. One advantage of this technique is that it allows students to develop critical thinking skills, since they must judge if the presented material adequately covers a specific topic. We describe the “Teaching is Learning” project, that implements these ideas. We first discuss two pilot studies: 1) since 2009, engineering students from the University of Chile have been creating and validating pedagogical 3D animations; and 2) during 2015, seventh graders from the Blest Gana secondary school created and evaluated pedagogical videos using their cell phones, editing the videos during their Technology class. We then give an overview of existing work on measuring student learning, and discuss how we can evaluate the effectiveness of our technique. We conclude by describing our plans for implementing the “Teaching is Learning” project at a larger scale during 2016, both in Chile and in Brazil.

Abstract A software company can define a software process line (SPrL) to deal with projects with different characteristics. This entails defining a base process and its variation points; the SPrL is then tailored to each project. This approach avoids the co‐evolution problems but is expensive to set up. In companies that register project events, this information could be used to discover the SPrL. However, traditional discovery algorithms focus on extracting a single process, which can be overly complex and would not be useful for managing future projects. Filtering out less frequent behavior leads to the discovery of simpler models, but these may not include relevant behavior. To address these issues, we propose the v‐algorithm, which discovers a SPrL from process logs. Two thresholds split the log into three clusters based on relation frequency. The first one is used to generate the base process, the second one is used to identify variable elements, and the last one is discarded. We used the v‐algorithm to discover the SPrL of Mobius, a small Chilean software company. We also discuss how the values of the thresholds affect the process discovery quality dimensions, extending existing metrics to the SPrL case. Copyright © 2016 John Wiley & Sons, Ltd.

Companies define software processes for planning and guiding projects. Since process definition is expensive, and in practice, no one process "fits all" projects, the current trend is to define a Software Process Line (SPrL): a base process that represents the common process elements, along with its potential variability. Specifying a SPrL is more expensive than just specifying one process, but the SPrL can be adapted to specific project contexts, minimizing the amount of extra work carried out by employees. Mining project logs has proven to be a promising approach for discovering the process that is applied in practice. However, considering all the possible variations that may be logged, the mined process may be overly complex. Some algorithms deal with this by filtering infrequent relations between log events, but they may discard relevant relations. In this paper we propose the v-algorithm that uses two thresholds to set up a SPrL: highly frequent relations are used to build the base process, variable relations define process variability, and rare relations are discarded as noise. We applied the $v$-$algorithm$ to the project log of Mobius, a small Chilean software company. We obtained a SPrL where we identified unexpected alternative ways of performing certain activities, as well as an optional activity that was originally specified as mandatory.

Companies formalize software processes as a way of organizing development projects. Since there are differences in project contexts, a one-size-fits-all approach does not work well in practice. Some companies use a family of a predefined processes, but this approach has a high process maintenance cost. Instead, we define Software Process Lines (SPrL), where a general process with variability is tailored to project contexts. Model- Driven Engineering (MDE) provides a formal framework for defining the models and transformations required for automated SPrL tailoring. However, this approach requires the definition and co-evolution of various types of models and tool support beyond the skills of process engineers, making the industrial adoption challenging. This paper shares our experience using a megamodeling approach to the development of the back-end of our toolset. The megamodel provides a uniform mechanism for process definition, variability, tailoring and evolution, and we hide the MDE complexity through a user-friendly front-end. We report the application of our approach at Mobius, a small Chilean software enterprise.

Companies formalize their software processes as a way of organizing their development projects. As each project has its own requirements and objectives, a family of processes is required in practice, in order to ensure that each project is handled appropriately. This family may be a collection of predefined processes, but can also be automatically generated by tailoring a general process to a project’s context which requires formalization and tool support to be successful. Model-driven engineering provides a formal framework for defining the models and transformations required for automated process tailoring, but various types of models must be specified and evolved, limiting the industrial adoption of this approach. To address this problem, in this paper we propose a megamodel for automated process tailoring. Megamodeling provides an integrating framework for modeling in the large, including the definition and evolution of all models and transformations required for tailoring while hiding complexity. We report the application of our approach to the software development process of Rhiscom, a small Chilean company.

We describe a RUntime MOnitoring and Recovery framework (RuMoR) for BPEL applications. Our tool checks for behavioral conformance with respect to a set of user-specified properties. When runtime violations are discovered, RuMoR automatically proposes and ranks recovery plans which users can then select for execution. These plans are generated using an adaptation of a SAT-based planning technique.

Web service applications are dynamic, highly distributed, and loosely coupled orchestrations of services which are notoriously difficult to debug. In this paper, we describe a user-guided recovery framework for web services. When behavioural correctness properties (safety and bounded liveness) of an application are violated at runtime, we automatically propose and rank recovery plans which users can then select for execution. For safety violations, such plans essentially involve "going back" -- compensating the occurred actions until an alternative behavior of the application is possible. For bounded liveness violations, such plans include both "going back" and "re-planning" --guiding the application towards a desired behavior. We report on the implementation and our experience with the recovery system.

For a system of distributed processes, correctness can be ensured by (statically) checking whether their composition satisfies properties of interest. However, Web services are distributed processes that dynamically discover properties of other Web services. Since the overall system may not be available statically and since each business process is supposed to be relatively simple, we propose to use runtime monitoring of conversations between partners as a means of checking behavioral correctness of the entire Web service system. Specifically, we identify a subset of UML 2.0 Sequence Diagrams as a property specification language and show that it is sufficiently expressive for capturing safety and liveness properties. By transforming these diagrams to automata, we enable conformance checking of finite execution traces against the specification. We show how our language can be used to specify the Specification Property System (SPS) [1]. We describe an implementation of our approach as part of an industrial system. Finally, we discuss our experience of specifying and monitoring a number of properties from three existing applications.

Automated consistency checking of UML models becomes necessary as models grow in size and complexity. Since the UML metamodel does not enforce model consistency, there are no fixed guidelines on how to approach the consistency problem. Current solutions are generally partial. The translation of the metamodel and the user designed model into Description Logics has proved to provide a solution in detecting a large set of inconsistencies. In order to make this solution available to system designers, we have implemented MCC+, a UML model consistency checker, built as a plug-in for Poseidon for UML, and relying on Jena as a reasoning engine. Compared to other approaches, we propose a usable and scalable solution, interoperable with a known modeling tool. We show the application of MCC+ to a real world large example of a meshing tool.

When model-checking reports that a property holds on a model, vacuity detection increases user confidence in this result by checking that the property is satisfied in the intended way. While vacuity detection is effective, it is a relatively expensive technique requiring many additional model-checking runs. We address the problem of efficient vacuity detection for Bounded Model Checking (BMC) of LTL properties, presenting three partial vacuity detection methods based on the efficient analysis of the resolution proof produced by a successful BMC run. In particular, we define a characteristic of resolution proofs - peripherality - and prove that if a variable is a source of vacuity, then there exists a resolution proof in which this variable is peripheral. Our vacuity detection tool, VaqTree, uses these methods to detect vacuous variables, decreasing the total number of model-checking runs required to detect all sources of vacuity.

Automated consistency checking of UML models becomes necessary as models grow in size and complexity. Because the UML metamodel does not enforce model consistency, there are no guidelines as how to approach the consistency problem. Current solutions are partial and tools are mostly of academic nature. The translation of the metamodel and the user designed model into Description Logics has proved to be useful in detecting a large set of inconsistencies. We present MCC, a UML model consistency checker, built as a plug-in for Poseidon for UML, and relying on Racer as a reasoning engine. We propose a usable and scalable solution, interoperable with a known modeling tool.