This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed.

Variability testing techniques search for effective and manageable test suites that lead to the rapid detection of faults in systems with high variability. Evaluating the effectiveness of these techniques in realistic settings is a must, but challenging due to the lack of variability intensive systems with available code, automated tests and fault reports. In this article, we propose using the Drupal framework as a case study to evaluate variability testing techniques. First, we represent the framework variability using a feature model. Then, we report on extensive non-functional data extracted from the Drupal Git repository and the Drupal issue tracking system. Among other results, we identified 3,392 faults in single features and 160 faults triggered by the interaction of up to 4 features in Drupal v7.23. We also found positive correlations relating the number of bugs in Drupal features to their size, cyclomatic complexity, number of changes and fault history. To show the feasibility of our work, we evaluated the effectiveness of non-functional data for test case prioritization in Drupal. Results show that non-functional attributes are effective at accelerating the detection of faults, outperforming related prioritization criteria as test case similarity.

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Successful software evolves from a single system by adding and changing functionality to keep up with users' demands and to cater to their similar and different requirements. Nowadays it is a common practice to offer a system in many variants such as community, professional, or academic editions. Each variant provides different functionality described in terms of features. Software Product Line Engineering (SPLE) is an effective software development paradigm for this scenario. At the core of SPLE is variability modelling whose goal is to represent the combinations of features that distinguish the system variants using feature models, the de facto standard for such task. As SPLE practices are becoming more pervasive, reverse engineering feature models from the feature descriptions of each individual variant has become an active research subject. In this paper we evaluated, for this reverse engineering task, three standard search based techniques (evolutionary algorithms, hill climbing, and random search) with two objective functions on 74 SPLs. We compared their performance using precision and recall, and found a clear trade-off between these two metrics which we further reified into a third objective function based on F β , an information retrieval measure, that showed a clear performance improvement. We believe that this work sheds light on the great potential of search-based techniques for SPLE tasks.

Web Application Programming Interfaces (APIs) allow systems to interact with each other over the network. Modern Web APIs often adhere to the REST architectural style, being referred to as RESTful Web APIs. RESTful Web APIs are decomposed into multiple resources (e.g., a video in the YouTube API) that clients can manipulate through HTTP interactions. Testing Web APIs is critical but challenging due to the difficulty to assess the correctness of API responses, i.e., the oracle problem. Metamorphic testing alleviates the oracle problem by exploiting relations (so-called metamorphic relations) among multiple executions of the program under test. In this paper, we present a metamorphic testing approach for the detection of faults in RESTful Web APIs. We first propose six abstract relations that capture the shape of many of the metamorphic relations found in RESTful Web APIs, we call these Metamorphic Relation Output Patterns (MROPs). Each MROP can then be instantiated into one or more concrete metamorphic relations. The approach was evaluated using both automatically seeded and real faults in six subject Web APIs. Among other results, we identified 60 metamorphic relations (instances of the proposed MROPs) in the Web APIs of Spotify and YouTube. Each metamorphic relation was implemented using both random and manual test data, running over 4.7K automated tests. As a result, 11 issues were detected (3 in Spotify and 8 in YouTube), 10 of them confirmed by the API developers or reproduced by other users, supporting the effectiveness of the approach.