Context: Teaching algorithms and introductory programming is a recognized challenge, especially for people with visual impairments (PwVI) and blindness. Problem: There is a knowledge gap regarding the most effective methods and tools for teaching algorithms and programming to PwVI. Additionally, the evaluation techniques used to assess the effectiveness of these approaches, especially in the context of Human-Computer Interaction (HCI), are little exploited in the literature. Solution: This study systematically mapped the methods and tools used in teaching algorithms and programming concepts to PwVI and the evaluation techniques applied in HCI. The goal is to provide an updated overview and guidance for educators and developers of educational materials. Method: A systematic mapping of the literature was conducted, and 13 relevant studies were selected to extract teaching methods, such as tactile flowcharts and tangible programming kits. The primary evaluation techniques identified included user testing, questionnaires, and interviews. Summary of Results: The teaching methods identified are diverse, emphasizing sensory resources. The most common HCI evaluation techniques helped validate the usability and effectiveness of these tools. Contributions: This study maps teaching practices for algorithms and accessible programming for PwVI, offering practical guidelines to foster inclusion in Computer Science.

Summary Concurrent programming is increasingly present in modern applications. Although it provides higher performance and better use of available resources, the mechanisms of interaction between processes/threads result in a greater challenge for software testing activity. The nondeterminism present in those applications is one of the main issues during the test activity since the same test input can produce different possible execution paths, which may or not contain defects. The test data automatic generation can alleviate this problem, ensuring higher speed and reliability in software testing activity. This paper explores the automatic test data generation for concurrent programs through Genetic Algorithm, a bioinspired optimization technique, and proposes a test data generation approach for concurrent programs, called BioConcST, and a new operator for the selection of test subjects, called FuzzyST, which uses fuzzy logic. The approaches were evaluated in an experimental study towards their validation. The results showed that BioConcST is more promising than the other approaches at all analyzed levels. FuzzyST, together with Elitism and Tournament operators, provided the best results; however, it proved more suitable for concurrent programs of higher complexity.

Background: Cognitive-Driven Development (CDD) is a coding design technique that aims to reduce developers’ cognitive effort in understanding a given code unit (e.g., a class). By following CDD design practices, it is expected that the coding units to be smaller and, thus, easier to maintain and evolve. However, it is so unknown whether these smaller code units coded using CDD standards are easier to understand. Aims: This work aims to assess how much CDD improves code readability. Method: To achieve this goal, we conducted a two-phase study. We start by inviting professional software developers to vote (and justify their rationale) on the most readable pair of code snippets (from a set of 10 pairs); one of the pairs was coded using CDD practices. We received 133 answers. In the second phase, we applied the state-of-the-art readability model to the 10-pairs of CDD-driven refactorings. Results: We observed some conflicting results. On the one hand, developers perceived that seven (out of 10) CDD-driven refactorings were more readable than their counterparts; for two other CDD-driven refactorings, developers were undecided, while only in one of the CDD-driven refactorings, developers preferred the original code snippet. On the other hand, we noticed that only one CDD-driven refactorings has better performance readability, assessed by state-of-the-art readability models. Conclusions: Our results provide initial evidence that CDD could be an exciting approach for software design.

Background: Performance testing can estimate the capacity of a web service under requests. Decision-making on the ideal cloud service infrastructures for deploying specific cloud applications is challenging. Goal: Investigation on the performance of cloud infrastructure providers under different load levels. Method: An experimental study evaluated Amazon, Azure, Google, and IBM cloud infrastructure providers in terms of performance under Infrastructure as a Service (IaaS) perspective. Results: The results indicated satisfactory performance in response time and latency among most providers when subjected to up to 300 simultaneous threads. However, this effect decreases as the number of threads increases, and Apdex index value and level of user's satisfaction are significantly reduced under different load levels, mainly for the IBM provider. Besides, the error rate rises substantially at 400 threads, with more critical results for IBM and Amazon providers. Conclusions: Providers show distinct differences across metrics, and the data collected during testings reinforced the potential particularities of cloud infrastructure services for the choice of a provider. Although preliminary, such results can support software companies in selecting a proper infrastructure provider according to particular requirements.

Software separation into components is a recognition that human work can be improved by focus on a limited set of data. The growing complexity of software has always been a challenge to industry. Several approaches have been proposed to support code design based on architectural styles and code quality metrics. However, most research involving human cognition in software engineering is focused on the evaluation of programs and learning instead of how the source code could be developed under this perspective. This paper presents an approach called Cognitive-Driven Development (CDD) that is based on cognitive complexity measurements and Cognitive Load Theory. This strategy can reduce the cognitive overload of the developers through the limitation of intrinsic complexity points from source code. Some cognitive complexity metrics has been extended and guidelines are presented to calculate the intrinsic complexity points and how their limit can be adapted under certain quality criteria. Experimental studies are currently being conducted to evaluate the CDD. Preliminary results indicate that the approach can reduce the future effort for maintenance and fixing software faults.

Multi-tenancy is the key feature for every Software as a Service (SaaS), as it enables multiple customers, so-called tenants, to transparently share a system's resources reducing costs. Tenants can customize a system according to their particular needs, however, such a high level of complexity may open possibilities for a failure. In addition, there is a lack of a reference architecture for such applications and once the implementations differ significantly, ensuring that all executions flows have been verified without impacting the working features for other tenants is a complex task. The clear understanding of the possible faults is fundamental for the identification, tolerance and definition of appropriate testing techniques. This paper presents a preliminary fault taxonomy for multi-tenant cloud applications considering their foundational features. A literature review previously carried out, a survey with practitioners and analysis of some applications were performed to achieve this classification. In addition, an e-commerce called MtShop was developed for a case study. The expressiveness of the proposed taxonomy is illustrated with critical faults identified in the MtShop through the automated and parallel testing. We conclude with the benefits that our taxonomy can bring to testing, prediction and regression testing activity of multi-tenant cloud applications.

Background: SaaS (Software as a Service) is a services delivery model in Cloud Computing whose applications are remotely hosted by the service provider and available to customers on demand over the Internet.Multi-tenant Architecture (MTA) is an organizational pattern for SaaS that enables a single instance of an application to be hosted on the same hardware and accessed by multiple customers, so-called tenants, with the aim of lowering costs.Tenants are able to configure the system according to their particular needs.Objective: This research aims at the obtaining an overview of the challenges and research opportunities in MTA context for SaaS through a Systematic Mapping Study.Results: Eighty nine primary studies were selected for discussions on advances and opportunities for further investigations.The results showed the relevancy of MTA and pointed out the main research trends for next years in this topic.