Context: A growing number of researchers are investigating how Mining Software Repositories (MSR) approaches can support software architecture activities, such as architecture recovery, tactics identification, architectural smell detection, and others. However, as of today, it is difficult to have a clear view of existing research on MSR for software architecture. Objectives: The objective of this study is to identify, classify, and summarize the state-of-the-art MSR approaches applied to software architecture (MSR4SA). Methods: This study is designed according to the systematic mapping study research method. Specifically, out of 2442 potentially relevant studies, we systematically identify 151 primary studies where MSR approaches are applied to perform software architecture activities. Then, we rigorously extract relevant data from each primary study and synthesize the obtained results to produce a clear map of reasons for adopting MSR approaches to support architecting activities, used data sources, applied MSR techniques, and captured architectural information. Results: The major reasons to adopt MSR4SA techniques are about addressing industrial concerns like achieving quality attributes and minimizing practitioners’ efforts. Most MSR4SA studies support architectural analysis, while architectural synthesis and evaluation are not commonly supported in MSR4SA studies. The most frequently mined data sources are source code repositories and issue trackers, which are also commonly mined together. Most of the MSR4SA studies apply more than one mining technique, where the most common MSR techniques are: (source code analysis, model analysis, statistical analysis), (machine learning, NLP). Architectural quality issues and components are the mostly mined type of information. Conclusion: Our results give a solid foundation for researchers and practitioners towards future research and applications of MSR approaches for software architecture.

Abstract Context Robotic systems are known to perform computation-intensive tasks with limited computational resources and battery life. Such systems might benefit from offloading heavy workloads to the Cloud; however, in some cases, this implies high network traffic that degrades performance and energy consumption. Goal In this study, we aim at evaluating the impact of different computation offloading strategies on performance and energy consumption in the context of autonomous robots. Method We conduct two controlled experiments involving a robotic mission based on the Turtlebot3 robot and ROS 1. The mission consists of three tasks that are recurrent in robotics and good candidates for computation offloading in research, namely, SLAM mapping, navigation stack, and object recognition. Each of the tasks is either executed on board or offloaded in a full-factorial experiment design. The obtained measures are then statistically analyzed. Results The results show that offloading the object recognition task causes a more significant decrease in resource utilization and energy consumption than both SLAM mapping and navigation. However, object recognition affects the volume of network traffic significantly to the extent that it can easily cause network congestion. Conclusions In the context of our experiments (i.e.,those involving small-scale ground ROS-based mobile robots operating under WiFi networks), offloading object recognition is beneficial in terms of performance and energy consumption. Nevertheless, large network bandwidth needs to be available for object recognition offloading. While the image resolution and frame rate have a significant impact on not only the network traffic but also energy consumption and performance, these parameters need to be carefully set so that the results of this task can be always received in time, which is particularly crucial in real-time systems.

The Robot Operating System (ROS) has become the de-facto standard framework for robotics software, and a great part of commercial robots is expected to have at least one ROS package on board in the coming years. For good quality, robotics software should rely on strong software engineering principles. In this paper, we perform a systematic mapping study on several works in software engineering on ROS, published at the top software engineering and robotics venues. Our goal is to analyze and evaluate such state-of-the-art regarding its relevance to the robotics software industry. The potentially-relevant studies are subject to a rigorously defined selection process. This results in a set of 63 primary studies on software engineering research on ROS. Those primary studies are then qualitatively analyzed according to a rigorously-defined classification framework. The results are of interest to both researchers and practitioners: (i) we provide an up-to-date overview of the state of the art on software engineering research on ROS and its potential for industrial adoption, (ii) a broad discussion of the research area as a whole, and (iii) point out routes of action for a better alignment between research and industry.

Block-based languages have been used as a facilitator to teach programming to newcomer and end-user programming students. Another alternative is to abstract the programming domain by using educational robots. Such approaches face some challenges. Block-based languages are far different than conventional programming languages, resulting in an abrupt transition between the two paradigms. On the other hand, commercial educational robots are limited to predesigned projects, which bounds students’ creativity. In this work, we propose an intermediate language (between blocks and traditional language that focuses on Arduino, allowing a wide range and student-designed projects. Preliminary results show that our language is simpler than the native Arduino language and that it would be a preferred alternative for beginner students of a computer science undergraduate course.

In software engineering, energy awareness refers to the conscious design and development of software that is able to monitor and react to energy state. Energy awareness is the key building block for energy efficiency and for other quality aspects of robotics software, such as mission completion time and safety. However, as of today, there is no guidance for practitioners and researchers on how to architect robotics software with energy awareness in mind. The goal of this paper is to identify architectural tactics for energy-aware robotics software. Specifically, using a dataset of 339493 data points extracted from five complementary data sources (e.g., source code repositories, Stack Overflow), we identified and analyzed 97 data points that considered both energy consumption and architectural concerns. We then synthesized a set of energy-aware architectural tactics via thematic analysis. In this preliminary investigation we focus on two representative architectural tactics.

Robots are becoming more and more commonplace in many industry settings. This successful adoption can be partly attributed to (1) their increasingly affordable cost and (2) the possibility of developing intelligent, software-driven robots. Unfortunately, robotics software consumes significant amounts of energy. Moreover, robots are often battery-driven, meaning that even a small energy improvement can help reduce its energy footprint and increase its autonomy and user experience.In this paper, we study the Robot Operating System (ROS) ecosystem, the de-facto standard for developing and prototyping robotics software. We analyze 527 energy-related data points (including commits, pull-requests and issues on ROS-related repositories, ROS-related questions on StackOverflow, ROS Discourse, ROS Answers and the official ROS Wiki).Our results include a quantification of the interest of roboticists on software energy efficiency, 10 recurrent causes and 14 solutions of energy-related issues, and their implied trade-offs with respect to other quality attributes. Those contributions support roboticists and researchers towards having energy-efficient software in future robotics projects.

In this paper, we describe a computational approach for authorship attribution of literary texts in a multilingual environment. First, we define a set of syntactical features at text structural levels by considering the internal grammatical structure of sentences. The main idea is to extract syntactic functions from every word necessary to make up a sentence, such as subject and predicate. Such elements denote a writing pattern, tracking a stylometric profile for each author. Second, we gather literary texts in five languages: Portuguese, Spanish, French, German, and English. We analyze distinct scenarios through a series of experiments, taking into account writer-dependent and writer-independent approaches while using authorship verifica- tion and identification strategies. Furthermore, we evaluate the model behavior when the amount of information in each sample is varied and the impact of the reference samples. We use a support vector machine classifier in this process.

Elasticity is one of the main features of cloud-based systems (CBSs), where elastic adaptations, such as those to deal with scaling in or scaling out of computational resources, help meet performance requirements under varying workload. There is an industrial need to find configurations of elastic adaptations and workload that could lead to degradation of performance in a CBS, serving possibly millions of users. However, the potentially great number of such configurations poses a challenge: executing and verifying all of them on the cloud can be prohibitively expensive in both, time and cost. We present an approach to model elasticity adaptation due to workload changes as a classification tree model and consequently generate short test sequences of configurations that cover all T-wise interactions between parameters in the model. These test sequences, when executed, help us assess the performance of elastic CBS. Using MongoDB as a case study, test sequences generated by our approach reveal several significant performance degradations.

One of the main features of cloud computing is elasticity, where resource is (de-)allocated on demand and at system's runtime. Since elasticity is not trivial, testing cloud-based systems (CBS) is laborious. Among others, testers must set up elasticity parameters on cloud computing infrastructure, specify a sequence of resource variations, and drive CBS through this sequence. In this paper, we propose a Domain-Specific Language (DSL) aiming at reducing the tester's effort in writing and executing CBS elasticity testing. Our DSL abstracts test case specification from different cloud provider's libraries, making it portable. Experiments with two different case studies, a MongoDB replica set and a distributed web application, shows that our approach reduces the effort (in number of words) to write test cases, compared to dedicated libraries. We also see a reduced effort when running the same test case on multiple cloud providers.