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Identifier lexicon may have a direct impact on software understandability and reusability and, thus, on the quality of the final software product. Understandability and reusability are two important characteristics of software quality. REpresentational State Transfer (REST) style is becoming a de facto standard adopted by software organizations to build their Web applications. Understandable and reusable Uniform Resource Identifers (URIs) are important to attract client developers of RESTful APIs because good URIs support the client developers to understand and reuse the APIs. Consequently, the use of proper lexicon in RESTful APIs has also a direct impact on the quality of Web applications that integrate these APIs. Linguistic antipatterns represent poor practices in the naming, documentation, and choice of identifiers in the APIs as opposed to linguistic patterns that represent the corresponding best practices. In this paper, we present the Semantic Analysis of RESTful APIs (SARA) approach that employs both syntactic and semantic analyses for the detection of linguistic patterns and antipatterns in RESTful APIs. We provide detailed definitions of 12 linguistic patterns and antipatterns and define and apply their detection algorithms on 18 widely-used RESTful APIs, including Facebook, Twitter, and Dropbox. Our detection results show that linguistic patterns and antipatterns do occur in major RESTful APIs in particular in the form of poor documentation practices. Those results also show that SARA can detect linguistic patterns and antipatterns with higher accuracy compared to its state-of-the-art approach — DOLAR.

ElsevierGonzález Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM.; Scanniello, G. (2015 Context: Software architectures should be evaluated during the early stages of software development in order to verify whether the Non-Functional Requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in Software Product Line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development.Objective: We present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI. Method:The family of experiments was carried out by 92 participants: Computer Science Master's and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). Results:The main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results. Conclusions:The results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.

Code evolution, whether related to the development of new features, bug fixing, or refactoring, inevitably changes the quality of the code. One particular type of such change is the accumulation of Technical Debt (TD) resulting from sub-optimal design decisions. Traditionally, refactoring is one of the means that has been acknowledged to help to keep TD under control. Developers refactor their code to improve its maintainability and to repay TD (e.g., by removing existing code smells and anti-patterns in the source code). While the accumulation of the TD and the effect of refactoring on TD have been studied before, there is a lack of empirical evidence from industrial projects on how the different types of code changes affect the TD and whether specific refactoring operations are more effective for repaying TD. To fill this gap, we conducted an empirical study on an industrial project and investigated how Refactoring, Bug Fixing, and New Development affect the TD. We have analyzed 2, 286 commits in total to identify which activities reduced, kept the same, or even increased the TD, further delving into specific refactoring operations to assess their impact. Our results suggest that TD in the studied project is mainly introduced in the development of new features (estimated in 72.8 hours). Counterintuitively, from the commits tagged as refactoring, only 22.90% repay TD (estimated to repay 8.30 hours of the TD). Moreover, while some types of refactoring operations (e.g., Extract Method), help repaying TD, other refactoring operations (e.g., Move Class) are highly prone to introduce more TD.

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