Browsing by Author "Galindo-Gutierrez, Geraldine"

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    A manual categorization of new quality issues on automatically-generated tests
    (2023) Galindo-Gutierrez, Geraldine; Narea Carvajal, Maximiliano Agustín; Fernández, Blanco Alison
    Diverse studies have analyzed the quality of automatically generated test cases by using test smells as the main quality attribute. But recent work reported that generated tests might suffer from a number of quality issues not considered previously, thus suggesting that not all test smells have been identified yet. Little is known about these issues and their frequency within generated tests. In this paper, we report on a manual analysis of an external dataset consisting of 2,340 automatically generated tests. This analysis aimed at detecting new quality issues, not covered by past recognized test smells. We use thematic analysis to group and categorize the new quality issues found. As a result, we propose a taxonomy of 13 new quality issues grouped in four categories. We also report on the frequency of these new quality issues within the dataset and present eight recommendations that test generators may consider to improve the quality and usefulness of the automatically generated tests. As an additional contribution, our results suggest that (i) test quality should be evaluated not only on the tests themselves, but considering also the tested code; and (ii) automatically generated tests present flaws that are unlikely to be found in manually created tests and thus require specific quality checking tools.
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    On the use of statistical machine translation for suggesting variable names for decompiled code: The Pharo case
    (2024) Sandoval Alcocer, Juan Pablo; Camacho-Jaimes, Harold; Galindo-Gutierrez, Geraldine; Neyem, Hugo Andrés; Bergel, Alexandre; Ducassee, Stéphane
    Adequately selecting variable names is a difficult activity for practitioners. In 2018, Jaffe et al. proposed the use of statistical machine translation (SMT) to suggest descriptive variable names for decompiled code. A large corpus of decompiled C code was used to train the SMT model. Our paper presents the results of a partial replication of Jaffe’s experiment. We apply the same technique and methodology to a dataset made of code written in the Pharo programming language. We selected Pharo since its syntax is simple – it fits on half of a postcard – and because the optimizations performed by the compiler are limited to method scope. Our results indicate that SMT may recover between 8.9% and 69.88% of the variable names depending on the training set. Our replication concludes that: (i) the accuracy depends on the code similarity between the training and testing sets; (ii) the simplicity of the Pharo syntax and the satisfactory decompiled code alignment have a positive impact on predicting variable names; and (iii) a relatively small code corpus is sufficient to train the SMT model, which shows the applicability of the approach to less popular programming languages. Additionally, to assess SMT’s potential in improving original variable names, ten Pharo developers reviewed 400 SMT name suggestions, with four reviews per variable. Only 15 suggestions (3.75%) were unanimously viewed as improvements, while 45 (11.25%) were perceived as improvements by at least two reviewers, highlighting SMT’s limitations in providing suitable alternatives.