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opencc-by-4.0Mar 2020View details →

DATASET - AVALIAÇÃO EMPÍRICA DA GERAÇÃO AUTOMATIZADA DE TESTES DE SOFTWARE SOB A PERSPECTIVA DE TEST SMELLS

A constante busca pela qualidade sempre está em destaque na área de Engenharia de Software. Dentre as diversas disciplinas dedicadas a essa temática, o teste de software tem se estabelecido como uma das mais importantes, dado sua eficácia na identificação de defeitos, em momento prévio à liberação de sistemas de software para o mercado. O teste de software é atividade-chave para o desenvolvimento de software de qualidade. Entretanto, desenvolver testes é tão ou mais custoso do que desenvolver o código de produção. Uma alternativa para a redução dos custos associados ao teste de software se dá pelo uso intensivo de ferramentas de automação de testes. A proposta dessas ferramentas é reduzir o tempo de produção sem afetar a qualidade do código. Apesar dessa premissa, não é comum encontrar abordagens que incluam uma camada de verificação de qualidade dos testes gerados automaticamente, o que pode reduzir a confiabilidade da eficácia desses testes. Neste cenário, a proposta dessa dissertação é analisar empiricamente massas de dados de teste, sob a perspectiva de test smells, no sentido de avaliar a qualidade dos testes produzidos por ferramentas de geração automatizada de testes de software. Test smells são más escolhas no design dos testes e tem características sintomáticas e podem acarretar diminuição na qualidade dos sistemas. Considerando os test smells em código de teste, o estudo analisa os testes gerados por duas ferramentas amplamente aceitas pela comunidade de testes: Evosuite e Randoop. Um conjunto de vinte e um projetos de software de código aberto, disponíveis na plataforma Github foram considerados no estudo. A análise considerou a dispersão de test smells no código de teste desses projetos, bem como a existência de potenciais correlações entre test smells e as relações com as métricas estruturais. Como principais resultados, encontramos fortes correlações entre os test smells e as métricas de cobertura do código, diferenças significativas entre os dados encontrados nas suítes de testes geradas automaticamente e com os testes pré-existentes nos projetos avaliados.

opencc-by-4.0Jun 2022View details →

Replication package and appendixes for Causal inference of server- and client-side code smells in web apps evolution

-Analysis  --R scripts used to make the analisys, divided by folders --Data folders used in the questions -Appendixes - used in the article to shwo extra tables and plots -data folders - Aggregation of data, each app has two files, CSV and xls -separated data folders - 5 files for each app, with lines corresponding to the each released official version --serversmells --clientsmells --javascriptsmells --Cloc(metrics) --version (all oficial releases) -issues_bugs --data -issues by app by release  --data_bugs_more - the same but only bugs, by app by release --scripts - scrips used to aggregate issues (from daily issues to by release) anf the same for bugs

opencc-by-4.0Nov 2021View details →

Reproduction package for paper "How far are we from reproducible research on code smell detection? A systematic literature review"

Checklist and data extracted from publications analyzed for "How far are we from reproducible research on code smell detection? A systematic literature review" paper, together with processing scripts and calculations of Cohen's Kappa. Paper that describes details of the data is available here: https://doi.org/10.1016/j.infsof.2021.106783

opencc-by-4.0May 2022View details →

Towards a systematic approach to manual annotation of code smells - C# Dataset of Long Method and Large Class code smells

This dataset includes open-source projects written in C# programing language, annotated for the presence of Long Method and God Class code smells. Each instance was manually annotated by at least two annotators. We explain our motivation and methodology for creating this dataset in our <a href="https://www.techrxiv.org/articles/preprint/Towards_a_systematic_approach_to_manual_annotation_of_code_smells/14159183/1">preprint</a>: Luburić, N., Prokić, S., Grujić, K.G., Slivka, J., Kovačević, A., Sladić, G. and Vidaković, D., 2021. Towards a systematic approach to manual annotation of code smells.  The dataset contains two excel datasheets: <ul> <li>DataSet_Large Class.xlsx – C# classes annotated for the Large Class code smell severity.</li> <li>DataSet_Long Method.xlsx – C# methods annotated for the Long method code smell severity.</li> </ul>  The columns in the datasheet represent: <ul> <li>Code Snippet ID – the full name of the code snippet.  <ul> <li>For classes, this is the package/namespace name followed by the class name. The full name of inner classes also contains the names of any outer classes (e.g., namespace.subnamespace.outerclass.innerclass).</li> <li>For methods, this is the full name of the class and the methods’s signature (e.g., namespace.class.method(param1Type, param2Type) ).</li> </ul> </li> <li>Link – The GitHub link to the code snippet, including the commit and the start and end LOC.</li> <li>Code Smell – code smell for which the code snippet is examined (Large Class or Long Method).</li> <li>Project Link – the link to the version of the code repository that was annotated.</li> <li>Metrics – a list of metrics for the code snippet, calculated by our <a href="https://github.com/Clean-CaDET/platform#readme">platform</a>. Our dataset provides 25 class-level metrics for Large Class detection and 18 method-level metrics for Long Method detection The list of metrics and their definitions is available <a href="https://github.com/Clean-CaDET/platform/blob/c4acff95ec00ff6c25fa62dde4818c1f40e39d39/CodeModel/CaDETModel/CodeItems/CaDETMetrics.cs">here</a>.</li> <li>Final annotation – a single severity score calculated by a majority vote. </li> <li>Annotators – each annotator's (1, 2, or 3) assigned severity score.</li> </ul> To help guide their reasoning for evaluating the presence and the severity of a code smell, three annotators independently annotated whether the considered heuristics apply to an evaluated code snippet. We provide these results in two separate excel datasheets: <ul> <li>LargeClass_Heuristics.xlsx - C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> <li>LongMethod_Heuristics.xlsx - C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> </ul> The columns of these two datasheets are: <ul> <li>Code Snippet ID - the full name of the code snippet (matching the IDs from DataSet_Large Class.xlsx and DataSet_Long Method.xlsx)</li> <li>Annotators – heuristics labelled by each of the annotators (1, 2, or 3).</li> <li>Heuristics – whether the heuristic is applicable to the examined code snippet or not (Section 1.2.4 lists heuristics relevant for the Large Class detection, and Section 1.2.5 lists the heuristics relevant for the Long Method detection).</li> </ul>

opencc-by-4.0Sep 2019View details →

Extremely Imbalanced Smell-based Defect Prediction

Abstract: In continuous integration/continuous delivery, one of the main requirements for high-speed delivery of software is to find bugs efficiently. For this reason, multiple solutions were introduced in the literature. For instance, defect prediction approaches based on bad code smells detected in modules from each version of the software. Nevertheless, these approaches do not consider the problem where there may exist an extremely higher percentage of non-defective modules compared to defective modules. Given that, each version of the software may only have a small number of defects. As a result, in this thesis, we introduce a new model with an autoencoder algorithm that uses design and implementation smells to detect defective modules. Therefore, we trained five autoencoders with distinct architectures. Ad- ditionally, for evaluation, we compared each model against autoencoders with the same architecture, trained with traditional object-oriented metrics and the combination of both. Our analysis did not show promising results, as the use of only smells and the combination of features did not provide an improve- ment compared with the use of metrics. However, we introduce a starting point for smell-based defect prediction in the context of dataset imbalance. Furthermore, we introduce a baseline for future work.   Dataset Description: We provide three datasets. The first results from the extraction of traditional object-oriented metrics (metric.csv). The second results from the extraction of design and implementation smells (smell.csv). The third is the combination of all the features (metricsmell.csv). Moreover, these features were extracted from Designite and Bugsdorjar software archives.

edi44/100

Interpreting the smells of predation: How alarm cues and kairomones induce different prey defenses.

1. For phenotypically plastic organisms to produce phenotypes that are well matched to their environment, they must acquire information about their environment. For inducible defences, cues from damaged prey and cues from predators both have the potential to provide important information, yet we know little about the relative importance of these separate sources of information for behavioural and morphological defences. We also do not know the point during a predation event at which kairomones are produced, i.e. whether they are produced constitutively, during prey attack or during prey digestion. 2. We exposed leopard frog tadpoles (Rana pipiens) to nine predator cue treatments involving several combinations of cues from damaged conspecifics or heterospecifics, starved predators, predators only chewing prey, predators only digesting prey or predators chewing and digesting prey. 3. We quantified two behavioural defences. Tadpole hiding behaviour was induced only by cues from crushed tadpoles. Reduced tadpole activity was induced only by cues from predators digesting tadpoles or predators chewing + digesting tadpoles. 4. We also quantified tadpole mass and two size-adjusted morphological traits that are known to be phenotypically plastic. Mass was unaffected by the cue treatments. Relative body length was affected (i.e. there were differences among some treatments), but none of the treatments significantly differed from the no-predator control. Relative tail depth was affected by the treatments and deeper tails were induced only when tadpoles were exposed to cues from predators digesting tadpoles or cues from predators chewing + digesting tadpoles. 5. These results demonstrate that some prey species can discriminate among a diverse set of potential cues from heterospecific prey, conspecific prey and predators. Moreover, the results illustrate that the cues responsible for the full suite of behavioural and morphological defences are not induced by tadpole crushing nor

openCC (other)Jun 2024View details →

opencc-by-4.0Aug 2020View details →

Replication package of "Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities"

Abstract: Test smells attempt to capture design issues in test code that reduce their maintainability. Previous work found such smells to be highly common in automatically generated test-cases, but based this result on specific static detection rules; although these are based on the original definition of “test smells”, a recent empirical study showed that developers perceive these as overly strict and non-representative of the maintainability and quality of test suites. This leads us to investigate how effective such test smell detection tools are on automatically generated test suites. In this paper, we build a dataset of 2,340 test cases automatically generated by EVOSUITE for 100 Java classes. We performed a multi-stage, cross-validated manual analysis to identify six types of test smells and label their instances. We benchmark the performance of two test smell detection tools: one widely used in prior work, and one recently introduced with the express goal to match developer perceptions of test smells. Our results show that these test smell detection strategies poorly characterized the issues in automatically generated test suites; the older tool’s detection strategies, especially, misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice; and (ii) more accurate detection strategies, to be evaluated primarily in industrial contexts.

opencc-by-4.0Oct 2020View details →

Exploring Design Smells for Smell-Based Defect Prediction

The archived file datasets.zip includes the datasets used for supporting the conclusions in the article Exploring Design Smells for Smell-Based Defect Prediction. In this paper, we answer two research questions: RQ1. Do Design code smells contribute to the performance of defect prediction models trained with Traditional code smells? RQ2. How do the different categories of Design smells impact the performance of the defect prediction models? Therefore, after extracting the archived file documents, you will find two sub-directories, respectively named "RQ1" and "RQ2". They include the results obtained for each one of the research questions, thus supporting our conclusions. (You will also find a README.pdf file with these same instructions regarding the datasets.) Inside "RQ1," you will find two directories, respectively named "configuration_1" and "configuration_2". They represent the different configurations for the experiments. "configuration_1" contains the datasets with results for the ten classifiers configurations with the highest scores and "configuration_2" contains the datasets with the results classifier configuration with the overall best results - Support Vector Machine with C=0.1. Furthermore, within each directory, there are three sub-directories, respectively named "designite," "designite_traditional," and "traditional." These have the datasets for each of the considered smell sets in our study. Inside "RQ2," you will find four directories. Each corresponds to a category from the design smells for the dataset "designite_traditional." These datasets were build from the same configuration as "configuration_2". Then, within every directory, there are 97 sub-directories representing the 97 projects analyzed in this study. Every project folder follows the same structure, which we define as follows. <ul> <li>The "dataset" directory contains the original training and testing dataset used.</li> <li>The "oversamples" directory contains the training dataset after oversampling for each of the feature selection approaches.</li> <li>The "score_summary" directory contains all classifier configurations considered, not only the 10 with the highest scores.</li> <li>The "scores.csv" file contains all the scores for the main classifier configurations studied in the particular experiment.</li> <li>The "selected_features" directory contains the selected features' information and the selected features dataset for each feature_selection method.</li> <li>The "selected_testing_X" directory contains the testing datasets.</li> <li>The "top_scores_summary" directory contains the classifier configurations and hyper-parameter scores for the top 10 highest scores.</li> </ul>

FIG. 5. — A in Greek khrόmis between sound and smell. Anthropozoology of a fish

FIG. 5. — A, Chromis chromis (Linnaeus, 1758) and B, Umbrina cirrosa (Linnaeus, 1758) as reproduced in Aldrovandi (1613) after the original engravings in, respectively, Rondelet (1554) and Belon (1553). Notice the lack of proportion (common length for Chromis chromis is 13 cm vs 40 cm for Umbrina cirrosa; cf. Froese & Pauly 2016). Image courtesy of Biblioteca Digitale Università di Bologna.

FIG. 3 in Greek khrόmis between sound and smell. Anthropozoology of a fish

FIG. 3. — Shi drums (Umbrina cirrosa (Linnaeus, 1758)) in their natural environment (Image courtesy of Laguna Project).

FIG. 4 in Greek khrόmis between sound and smell. Anthropozoology of a fish

FIG. 4. — Brown meagres (Sciaena umbra Linnaeus, 1758) in their natural environment (Image E. Sáez Goñalons & V. Martínez Moll, wikimedia.org; CC BY 3.0).

FIG. 2. — A in Greek khrόmis between sound and smell. Anthropozoology of a fish

FIG. 2. — A, Round-based zither (kithára). Detail from a red-figure vase painting, about 430 BCE – cf. Lexicon Iconographicum Mythologiae Classicae, Apollon 691a (Image by the author); B, Wide-eyed flounder, Bothus podas (Delaroche, 1809), a fish called kítharos by the ancient Greeks for its similarity to a roundbased kithára. Cf. Guasparri 2005: 216 (Image courtesy of Laguna Project).

FIG. 1 in Greek khrόmis between sound and smell. Anthropozoology of a fish

FIG. 1 — Definiens and definiendum: a human finger compared to Solen marginatus Pulteney, 1799, one among other mollusks called dáktulos (lit. 'finger'), or όnux (lit. 'nail') in Ancient Greek – e.g., Thompson 1947: 184 (Image by the author).

FIG. 6. — A in Greek khrόmis between sound and smell. Anthropozoology of a fish

FIG. 6. — A Mediterranean damselfish (Chromis chromis (Linnaeus, 1758)) in its natural environment (Image A. Kok, wikimedia.org;

opencc-by-4.0May 2024View details →

Dataset: an empirical study on architectural smells through a pipeline for continuous technical debt assessment

<h2>Dataset of the study "An empirical study on architectural smells through a pipeline for continuous technical debt assessment"</h2> <h3>Abstract</h3> In recent years, researchers spent an increasing amount of effort investigating technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic. However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings. In response to this concern, researchers strive to expand the scope of their studies by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study. To address this issue, this paper presents an approach for tackling this problem and enabling researchers to study architectural smells, a well-known indicator of architectural technical debt,  at a large scale. Specifically, we introduce a novel approach to a data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets with any static analysis tool. Finally, we use the data collected through the pipeline to study the correlation between architectural smells and logical coupling in order to understand how smells influence maintenance efforts.

opencc-by-4.0Feb 2022View details →

Mapeo entre Code Smells-AntiPatterns seleccionados para nuestra investigación.

Planilla de mapeo entre Code Smells-AntiPatterns seleccionados para la investigación: Análisis de defectos de diseño; Como parte del informe de Proyecto de Grado presentado al tribunal evaluador como requisito de graduación de la carrera Ingeniería en Computación de la Universidad de la República.

opencc-by-4.0Apr 2015View details →

Landfill: An open dataset of code smells with public evaluation

Code smells are symptoms of poor design and implementation choices that may hinder code comprehension and possibly increase the change- and fault-proneness of source code. Several techniques have been proposed in the literature for detecting code smells. These techniques are generally evaluated by comparing their accuracy on a set of detected candidate code smells against a manually-produced oracle. Unfortunately, such comprehensive sets of annotated code smells are not available in the literature, with only a few exceptions. This dataset provides 243 instances of five types of code smells identified from 20 open-source software projects. In particular, it contains a SQL file with the information concerning such instances and a zip file with their source code.