Context: There are several initiatives to semi-automate the initial selection of studies task for Systematic Literature Reviews (SLR) to reduce effort and potential bias. Objective: We propose a strategy called SCAS-AI to semi-automate the initial selection task. This strategy improves the original SCAS strategy with Artificial Intelligence (AI) resources (fuzzy logic and genetic algorithm) for studies selection. Method: We evaluated the SCAS-AI strategy through a quasi-experiment with SLRs in Software Engineering (SE). Results: In general, the SCAS-AI strategy improved the results achieved using the original SCAS strategy in reducing the effort of the initial selection task. The effort reduction applying SCAS-AI was 39.1%. In addition, the errors percentage was 0.3% for studies automatically excluded (false negative – loss of evidence) and 3.3% for studies automatically included (false positive – evidence later excluded during the full-text reading). Conclusion: The results show the potential of the investigated AI techniques to support the initial selection task for SLRs in SE.

Context: Systematic Review (SR) is a methodology used to find and aggregate relevant existing evidence about a specific research topic of interest. It can be very time-consuming depending on the number of gathered studies that need to be analyzed by researchers. One of the relevant tools found in the literature and preliminarily evaluated by researchers of SRs is StArt, which supports the whole SR process. It has been downloaded by users from more than twenty countries. Objective: To present new features available in StArt to support SR activities. Method: Based on users' feedback and the literature, new features were implemented and are available in the tool, like the SCAS strategy, snowballing techniques, the frequency of keywords and a word cloud for search string refining, collaboration among reviewers, and the StArt online community. Results: The new features, according to users' positive feedback, make the tool more robust to support the conduct of SRs. Conclusion: StArt is a tool that has been continuously developed such that new features are often available to improve the support for the SR process. The StArt online community can improve the interaction among users, facilitating the identification of improvements and new useful features.

Context: Systematic Review (SR) is a well-defined and rigorous methodology used to find relevant evidence about a specific topic of interest. Depending on the number of identified primary studies, the selection activity can be very time-consuming and a strategy, like SCAS, to semi-automate this activity can be helpful. Objective: To present an experimental study carried out to evaluate the SCAS strategy. Method: We conducted an experiment to compare the efficiency and effectiveness between participants using SCAS and the manual approach. They received necessary training for applying SCAS using tool support. They were divided into five groups, which conducted SRs based on their research areas. Results: When applying SCAS, the average effort reduction was 22.33%, and the average percentage error was 3.95% with a minimal loss of 1.6 evidence per SR. In addition, results showed an overall precision of 65.49% on an overall recall of 90.24% when using SCAS. The overall Kappa showed that there is a substantial agreement level between the groups and SCAS. Conclusion: The experiment increased the confidence in the strategy, reinforcing that it can reduce the effort required to select primary studies without adversely affecting the overall results of SRs.

This paper presents a tool named CRISTA that supports code comprehension through the application of stepwise abstraction. It uses a visual metaphor to represent the code and supports essential tasks for code reading, inspection and documentation. As code comprehension is time consuming, a tool can greatly improve such activity.

Experimental replications are very important to the advancement of empirical software engineering. Replications are one of the key mechanisms to confirm previous experimental findings. They are also used to transfer experimental knowledge, to train people, and to expand a base of experimental evidence. Unfortunately, experimental replications are difficult endeavors. It is not easy to transfer experimental know-how and experimental findings. Based on our experience, this paper discusses this problem and proposes a Framework for Improving the Replication of Experiments (FIRE). The FIRE addresses knowledge sharing issues both at the intra-group (internal replications) and inter-group (external replications) levels. It encourages coordination of replications in order to facilitate knowledge transfer for lower cost, higher quality replications and more generalizable results.

The establishment of a low-cost, effective testing and validation strategy has been pursued by many researchers at the program level as well as at the specification level. The application of mutation testing for validating specifications based on statecharts is proposed. A mutation operator set for statecharts, one of the crucial points for effectively applying mutation testing is defined; in this scope these operators can be taken as a fault model. We also provide strategies to abstract the statechart components according to different statechart features that may comprise the testing and validation activity aims, providing in this way mechanisms for the establishment of an incremental, hierarchical, mutation-based testing strategy. Implementation and functional aspects of PROTEUM/ST, a tool under development are also presented.

Proposes the application of the mutation analysis criterion in the context of specifications based on finite state machines. The main concepts of finite state machines and mutation analysis are briefly introduced. An experiment is reported which manually applies mutation analysis to a finite state machine modeling a Class 0 ISO transport protocol specification, using two test-sequence generator criteria-the W method and the TT (transition tours) method. The results obtained are presented, and evidences are given that the use of mutation analysis is effective in this context. Finally, the lines of evolution of the work presented in this paper are briefly discussed.>