Test Code Research Articles

B-127 An Automated Approach to Obtain Delta Check Limits

Abstract Delta checking as a systematic approach to quality control specimens was first described by Nosanchuk and Gottmann in 1974. The purpose is to detect specimen errors due to short-sample aspiration, mislabeled cup or sample, incorrect patient draw, problems in analysis, and/or clerical errors. Once inaccuracy is detected, rejection and specimen recollection are the most suitable approach. The delta check procedure compares a patient’s current test results to the previous test results from the same individual using a specified time interval and delta check limit (DCL). The DCL can be expressed as absolute change, percentage change, rate of change, or rate of percentage change. This procedure is typically applied to several analytes, comparing the absolute change with a specified DCL. DCLs are commonly derived from the literature and may be optimized over time based on local patient populations. Creating and modifying DCLs poses a problem, because changes in the DCL can result in flagging too little or too many specimens, and often DCLs don’t take into consideration patient location and therefore lack specificity. Here, we describe a software tool that allows the determination of DCLs based on the individual laboratory’s or ordering unit’s patient results derived from the LIS. The software functionality was tested on a patient dataset from a large county hospital in California, USA. The software allows import of patient data for analysis and creation of population-specific DCLs. Typically, inpatient data is used, as consecutive data points within a common delta check timeframe (hours to days) are less expected for outpatients. The tool allows selection of an unlimited number of analytes of interest to be included in the DCL assessment. Furthermore, the underlying DCL timeframe that compares the two different results is customizable. Lastly, the tool provides the expected number of DCL flags based on the chosen cutoffs. This allows for proper resource allocation or adjustment of the DCL according to available resources. After the import of raw LIS data, an automatic data cleanup is performed. Then the assays of interest and, if available, the associated patient location are directly selected in the software. The dashboard provides each assay test code that is part of the delta checking process, the desired DCL, and the chosen delta check time period (either in hours or days). The resulting expected number of DCL flags is provided specifically for each test, per hour and per day. Any change to the DCL is displayed on the dashboard in real time. Delta checks are a tool to detect specimen inaccuracies resulting from pre-analytical or analytical errors. This software tool provides customized DCLs based on patient population and the individual laboratory instead of deriving them from the literature. This novel customization enables simple derivation of DCLs that are patient population-specific and optimized for each laboratory’s resources. This allows for improved detection of specimen inaccuracies and ultimately leads to improved patient care. *The product is currently under development. Its future availability cannot be guaranteed. Please contact your local Siemens Healthineers organization for further details.

Quantifying the Prevalence of Acute Vestibular Neuritis through Big Data Analysis

Objectives: Big data analytics in healthcare research have gained momentum, offering unprecedented opportunities to investigate complex medical conditions like acute vestibular neuritis (AVN). However, an inappropriate definition can introduce bias and inaccuracies into prevalence estimation, making the results unreliable and hindering cross-study comparisons. The Health Insurance data in South Korea will be used to create a robust operational definition for AVN.Methods: The study utilized the National Patients Sample dataset from the Health Insurance Review and Assessment Service (HIRA) of the Republic of Korea. The operational definition of AVN was defined using the HIRA data, which includes specific codes for diagnosis, testing, and medications. The revised categorization scheme for AVN was presented as case 1 through case 5, with criteria for each category.Results: The optimal conditions are deemed to be those that encompass the outcomes of both case 5 and case 1-1, encompassing all conditions. The study also provided prevalence estimates for subgroups based on demographic factors (age, sex), and found a consistent pattern throughout all years, sex, and age.Conclusions: The study analyzed the prevalence of AVN in case 1 and case 5, which were similar to the reference prevalence of 3.5 per 100,000 people reported in other countries. The study’s results are encouraging for several reasons, including the validity of the operational definitions used, and the agreement between the study’s prevalence estimates and the reference prevalence. The operational definition in statistics, in the context of big data, serves as a precise and standardized criterion.

An Introductory Synthetic Data Tool

ObjectivesSynthetic data reproduces features of a dataset without disclosing sensitive information, allowing researchers to explore data structures and test code without requiring access to real, potentially sensitive, data. We produced a low-fidelity synthetic data generation tool, accompanied by extensive documentation, allowing novice and expert users to produce such data.
 MethodsOur tool, consisting of a Python notebook and a user guide, takes a dataset as input, and produces ‘low-fidelity’ synthetic copy of this dataset, recreating the data fields (or columns) of a dataset, as well as the data types and statistical relationships within these fields, but not between them. It has been tested using real-world administrative data sets and with several users, looking at the quality of the data generated, inspecting whether the data is indeed low-fidelity (i.e. statistical relationships between fields are not recreated) and the usability of the tool.
 ResultsOur tool successfully created synthetic datasets from administrative datasets. Users were positive about its usability and the generated data. Tests indicated that computational memory is a main constraint on the size of datatable that can be read in by the tool. We have since implemented improvements to the memory efficiency of the tool to partially address this and have also added procedures that allow for using subsets instead of complete datasets, allowing for the use of datasets which would have otherwise been too large to be used. Testing further indicated that, while the tool by design does not preserve any relationships between fields, they can be reproduced by coincidence, and a limited disclosure process may be required when correlations from the original data are reproduced.
 ConclusionsThe tool is easy to use and therefore a useful introduction to synthetic data, providing users with a foundation before using more sophisticated synthetic data tools like Synthpop. Future work could include the development of a Python library and extension of the tool to handle linked datatables.

Prediction of cold start emissions for hybrid electric vehicles based on genetic algorithms and neural networks

The emission of hybrid electric vehicles deteriorates during cold start, and it is a cost-effective method to reduce pollutant emissions during cold start of hybrid electric vehicles through energy management strategies. The development of energy management strategies for hybrid electric vehicle requires an accurate cold-start emissions model. However, traditional cold-start emission models based on statistics and experience are difficult to achieve satisfactory prediction accuracy, and require a lot of experimental cost and time. The use of neural networks for cold start emission prediction of hybrid electric vehicles has been widely used because of its low cost and high prediction accuracy. In this paper, the genetic algorithm is used to optimize the weights and thresholds of the neural network, and a neural network cold-start emission prediction model for hybrid electric vehicles is built. We trained the neural network using the high temperature (40 °C), normal temperature (23 °C) and low temperature (−20 °C) cold start data of hybrid vehicles obtained in the World Light Vehicle Test Code (WLTC), studied the cold start prediction results under the different cold start temperature, studied the cold start prediction results under the different input parameters, studied the cold start prediction results of the neural network optimized by the genetic algorithm. Under different input parameters and cold start temperature, the GA-BP algorithm can accurately predict the emissions of carbon monoxide (CO), carbon dioxide (CO2), hydrocarbons (THC), nitrogen oxides (NOx) and particulate matter number (PN) under hybrid electric vehicles.

Impact of SARS-CoV-2 Infection on Disease Trajectory in Youth with T1D: An EHR-Based Cohort Study from the RECOVER Program

Background. Postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) is associated with worsening diabetes trajectory. It is unknown whether PASC in children with type 1 diabetes (T1D) manifests as worsening diabetes trajectory. Objective. To explore the association between SARS-CoV-2 infection (COVID-19) and T1D-related healthcare utilization (for diabetic ketoacidosis (DKA) or severe hypoglycemia (SH)) or hemoglobin (Hb) A1c trajectory. Methods: We included children <21 years with T1D and ≥1 HbA1c prior to cohort entry, which was defined as COVID-19 (positive diagnostic test or diagnosis code for COVID-19, multisystem inflammatory syndrome in children, or PASC) or a randomly selected negative test for those who were negative throughout the study period (Broad Cohort). A subset with ≥1 HbA1c value from 28 to 275 days after cohort entry (Narrow Cohort) was included in the trajectory analysis. Propensity score-based matched cohort design followed by weighted Cox regression was used to evaluate the association of COVID-19 with healthcare utilization ≥28 days after cohort entry. Generalized estimating equation (GEE) models were used to measure change in HbA1c in the Narrow Cohort. Results. From March 01, 2020 to June 22, 2022, 2,404 and 1,221 youth met entry criteria for the Broad and Narrow Cohorts, respectively. The hazard ratio for utilization was (HR 1.45 (95% CI: 0.97, 2.16)). In the Narrow Cohort, the rate of change (slope) of HbA1c increased 91–180 days after cohort entry for those with COVID-19 (0.138 vs. −0.002, p = 0.172 ). Beyond 180 days, greater declines in HbA1c were observed in the positive cohort (−0.104 vs. 0.008 per month, p = 0.024 ). Conclusion. While a trend toward worse outcomes following COVID-19 in T1D patients was observed, these findings were not statistically significant. Continued clinical monitoring of youth with T1D following COVID-19 is warranted.

Analysis of Fracture Characteristics of Ore Rock Based on GMTS Criterion

In this paper, the Tongkaiyu ore-rock is taken as the research object, aiming to analyze the fracture characteristics of the mixed type I, II and I–II faults in ore-rock by using the appropriate fracture criterion, and analyze the fracture development mode. Considering the non-singular term (T stress) in the formula of stress field sequence at crack tip, the generalized maximum tangential stress (GMTS) criterion is established, which is consistent with the boundary conditions of the central straight Brazilian disk (CSTBD) specimen. Through CSTBD splitting test and three-dimensional particle flow code (PFC3D) numerical simulation, the Angle between the central direct joint and the loading direction was changed, and the fracture toughness and fracture development law of type I, type II and type I–II mixed fracture were obtained. The theoretical and simulation results show that, compared with the maximum tangential stress (MTS) criterion, the GMTS criterion has an advantage in judging the fracture characteristics of mixed types I, II and I–II in ore rock.

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. We implement our approach in the Xscope framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. Xscope is able to identify inputs that trigger exceptions in about 73% of the tested functions.

Risk and Severity of COVID-19 Infection in Monoclonal Gammopathy of Undetermined Significance: A 3-Year Propensity Matched Cohort Study.

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant disorder causing monoclonal plasma cell proliferation in bone marrow. This population is at risk of developing multiple myeloma (MM) and severe viral infections; risk factors of severe COVID-19 infection. Using TriNetX, a global platform providing data of 120 million patients, we aimed to quantify the risk and severity of COVID-19 in MGUS patients. A retrospective cohort analysis was performed using the TriNetX Global Collaborative Network. From January 20, 2020, to January 20, 2023, we identified a cohort of 58,859 MGUS patients and compared to non-MGUS patients, determined by relevant diagnosis/LOINC test codes. After 1:1 propensity score-matching, we identified COVID-19 cases to quantify risk and identify patients who had been hospitalized, ventilated/intubated, and deceased to quantify severity. Measures of association and Kaplan-Meier analysis were conducted. After propensity-score matching, there were 58,668 patients in both cohorts. MGUS patients were found to be at a reduced risk of contracting COVID-19 (RR 0.88, 95% CI 0.85-0.91). MGUS patients with COVID-19 showed higher mortality risk and decreased survival time compared to the general population (HR 1.14, 95% CI 1.01-1.27). MGUS patients with COVID-19 who were hospitalized exhibited significantly decreased survival time (log-rank test, P=0.04). As COVID-19 remains a looming health concern, especially amongst vulnerable populations, our analysis emphasizes the need for adequate vaccination and treatment regimens as well as an understanding of the severity of infection in MGUS patients and justification for precautionary measures.

Perancangan Aplikasi Naviku untuk Memberikan Informasi Navigasi Kepada Tunanetra Menggunakan Metode Test Driven Development

Visually impaired is a term for someone who has lost or reduced the function of vision. People with visual disabilities often experience difficulties in daily activities, for example when walking or navigating. They need the help of others to help them go to a room or move from one place to another. Currently, existing navigation aids do not fully meet the needs of the blind. They still need the help of others to get detailed information about visual objects such as the layout of rooms in buildings, traffic signs, and vehicles. Based on these problems, an Android-based navigation application needed that utilizes Text-to-Speech (TTS) technology to help blind people navigate independently by utilizing navigation information provided by voice. In order to help the development process, applications are developed by adopting Agile Software Development principles using the Test Driven Development (TDD) method. The features developed begin with a test fail phase where the developer creates unit test code before the production code is implemented, then refactor the code, and tests afterward (test passes). The success status of the features being tested gets success status with a rate of 100% for all features. Then in the TTS test, testing was carried out with 2 TTS engines, namely Google TTS and Samsung TTS, the results showed that the Word Error Rate (WRE) of Google TTS was 2,86, lower than Samsung TTS which got 4,06.

NJN: A Dataset for the Normal and Jaundiced Newborns

Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood sample collection, which can inflict pain and distress on the patient, and may give rise to additional complications. Alternatively, a non-invasive method using image-processing techniques and implementing kNN, Random Forest, and XGBoost machine learning algorithms as a classifier can be employed to diagnose jaundice, necessitating a comprehensive database of infant images to achieve a diagnosis with high accuracy. This data article presents the NJN collection, a repository of newborn images encompassing diverse birthweights and skin tones, spanning an age range of 2 to 8 days. The dataset is accompanied by an Excel sheet file in CSV format containing the RGB and YCrCb channel values, as well as the status of each sample. The dataset and associated resources are openly accessible at Zenodo website. Moreover, the Python code for data testing utilizing various AI techniques is provided. Consequently, this article offers an unparalleled resource for AI researchers, enabling them to train their AI systems and develop algorithms that can assist neonatal intensive care unit (NICU) healthcare specialists in monitoring neonates while facilitating the fast, real-time, non-invasive, and accurate diagnosis of jaundice.

Extended Discrete-State Event-Driven Hardware-in-the-Loop Simulation for Power Electronic Systems Based on Virtual-Time-Ratio Regulation

This paper proposes an extended discrete-state event-driven (EDSED) controller hardware-in-the-loop (CHIL) method for power electronic systems, employing virtual time ratio (VTR) regulation to achieve CHIL function. Specifically, the proposed VTR in CHIL simulation is introduced to define the time ratio at which the mathematical model of the power circuit runs in the simulator. Additionally, by adjusting different VTRs, the data interaction between the simulator and controller is modified and employed as an event to trigger both simulation and control calculations, ensuring a precise correlation between simulation data and control information. The proposed EDSED CHIL simulation method is validated through CHIL and full hardware experiments involving a power electronic transformer and a multi-port power router. This paper offers an effective CHIL solution for testing control strategies and codes in power electronic systems characterized by complex coupling and high frequency.

Anomaly-GAN: A data augmentation method for train surface anomaly detection

Train surface anomaly detection is an essential task in vision-based railway safety inspection. Although existing deep learning methods show great potential, their anomaly detection accuracy is affected by the lack of abnormal images. The number of abnormal images on the train surface is far less than that of normal images. One effective way to solve this problem is to expand the abnormal sample. However, most of train surface anomaly image generation methods faces difficulties in producing images with high quality and rich diversity at the same time. In addition, generating small-area anomalies is not ideal. An Anomaly-GAN based on mask pool, abnormal aware loss, and local versus global discriminators is thus proposed in this paper to solve these problems. The mask pool consists of prior-knowledge-based masks and expert-experience-based masks that guide model in generating anomalies with different shapes, rotation angles, spatial locations, and part numbers. The anomaly aware loss focuses on small-area anomalies, thereby promoting the generated anomalies with more detailed textures and richer semantics. The local versus global consistency discriminators combine local and global feature expressions that lead to the generation of more realistic and natural abnormal samples. The experiments show that, compared with other advanced data augmentation algorithms, the images generated by Anomaly-GAN achieve the best FID and LPIPS scores in all anomaly categories. In addition, compared with the case without data augmentaion, the proposed data enhancement method improves the performance of CNN on mAP and mIOU by 25.6% and 24.2%, respectively. Test code is available in https://github.com/AI-Dream-Chaser/Anomaly-GAN.

Sensitivity and robustness analysis of adaptive neuro-fuzzy inference system (ANFIS) for shear strength prediction of stud connectors in concrete

The shear strength of stud connectors is essential for designing steel-concrete structures, which is assessed only through a push-out test or available design codes. An alternative technique that eliminates the need to conduct the push-out test is soft computing (SC). The performance of any machine learning (ML) based prediction model depends on the sensitive parameters used in the model development. This paper performs a sensitivity analysis on the shear strength prediction of stud connectors embedded in concrete. A system identification (SI) was conducted using an adaptive neuro-fuzzy inference system (ANFIS) to find the most sensitive combinations of input variables. Six different models were developed based on the SI results. Three machine learning algorithms, including ANFIS, extreme learning machine (ELM), and artificial neural network (ANN), were used to estimate the shear strength of stud connectors in each developed model. The results show that the number of studs (n) is the most sensitive parameter in predicting shear strength. Irrespective of concrete compressive strength (fc), the combination of the stud diameter (ϕ), number of studs, and stud spacing (s) can predict the shear strength with the accuracy of ±8.67 kN. The robustness of the three AI algorithms was evaluated using the Monte Carlo Simulation method. The individual conditional expectation (ICE) was also presented to visualize the correlation between the target shear strength and the six predictors. The results of this study show that sensitivity analysis is an essential tool for any data-driven ML model for accurate prediction.

The methods of Tetrads, Hexads, and Octads: A type of more powerful sensory discrimination methods

The well-known sensory discrimination method of Tetrads is a special case of the ‘M + N’ with M = N = 2. This paper extends the method of Tetrads to a general situation, i.e., the ‘M + N’ with M = N =k≥2. The methods of Hexads and Octads are the special cases of the ‘M + N’ with M = N = k, where k = 3, and 4, respectively. The general analytical psychometric functions for the specified and unspecified ‘M + N’ with M = N = k≥2 are produced. Simulation-derived psychometric functions for the specified and unspecified methods of Hexads and Octads are also produced. The values of the simulation-derived psychometric functions are well-matched with the values of the analytical psychometric functions. The performances of the methods of Hexads and Octads in both difference and similarity/equivalence tests are explored and compared with the performances of the method of Tetrads. It is shown that both difference and similarity/equivalence testing powers for the unspecified methods of Hexads and Octads are larger than those for the unspecified method of Tetrads. It suggests that the unspecified methods of Tetrads, Hexads, and Octads are a type of more powerful unspecified sensory discrimination methods. Tables and R codes are presented and provided for estimations of probability of correct response, Pc, Thurstonian discriminal distance δ or d´, B value for estimating variance of d´ for the specified and unspecified methods of Hexads and Octads. Tables and R codes for critical values, testing powers, and sample sizes for difference and similarity/equivalence tests using the specified and unspecified methods of Hexads and Octads are also presented and provided. The methods of Hexads and Octads are appropriate for visual and manual evaluations of food or non-food products, where sensory fatigue, carryover, or adaption effects are not mainly concerned.

A taxonomy of assets for the development of software-intensive products and services

Context:Developing software-intensive products or services usually involves a plethora of software artefacts. Assets are artefacts intended to be used more than once and have value for organisations; examples include test cases, code, requirements, and documentation. During the development process, assets might degrade, affecting the effectiveness and efficiency of the development process. Therefore, assets are an investment that requires continuous management.Identifying assets is the first step for their effective management. However, there is a lack of awareness of what assets and types of assets are common in software-developing organisations. Most types of assets are understudied, and their state of quality and how they degrade over time have not been well-understood. Methods:We performed an analysis of secondary literature and a field study at five companies to investigate and identify assets to fill the gap in research. The results were analysed qualitatively and summarised in a taxonomy. Results:We present the first comprehensive, structured, yet extendable taxonomy of assets, containing 57 types of assets. Conclusions:The taxonomy serves as a foundation for identifying assets that are relevant for an organisation and enables the study of asset management and asset degradation concepts.

Flakify: A Black-Box, Language Model-Based Predictor for Flaky Tests

Software testing assures that code changes do not adversely affect existing functionality. However, a test case can be flaky, i.e., passing and failing across executions, even for the same version of the source code. Flaky test cases introduce overhead to software development as they can lead to unnecessary attempts to debug production or testing code. Besides rerunning test cases multiple times, which is time-consuming and computationally expensive, flaky test cases can be predicted using machine learning (ML) models, thus reducing the wasted cost of re-running and debugging these test cases. However, the state-of-the-art ML-based flaky test case predictors rely on pre-defined sets of features that are either project-specific, i.e., inapplicable to other projects, or require access to production code, which is not always available to software test engineers. Moreover, given the non-deterministic behavior of flaky test cases, it can be challenging to determine a complete set of features that could potentially be associated with test flakiness. Therefore, in this article, we propose Flakify, a black-box, language model-based predictor for flaky test cases. Flakify relies exclusively on the source code of test cases, thus not requiring to (a) access to production code (black-box), (b) rerun test cases, (c) pre-define features. To this end, we employed CodeBERT, a pre-trained language model, and fine-tuned it to predict flaky test cases using the source code of test cases. We evaluated Flakify on two publicly available datasets (FlakeFlagger and IDoFT) for flaky test cases and compared our technique with the FlakeFlagger approach, the best state-of-the-art ML-based, white-box predictor for flaky test cases, using two different evaluation procedures: (1) cross-validation and (2) per-project validation, i.e., prediction on new projects. Flakify achieved F1-scores of 79% and 73% on the FlakeFlagger dataset using cross-validation and per-project validation, respectively. Similarly, Flakify achieved F1-scores of 98% and 89% on the IDoFT dataset using the two validation procedures, respectively. Further, Flakify surpassed FlakeFlagger by 10 and 18 percentage points (pp) in terms of precision and recall, respectively, when evaluated on the FlakeFlagger dataset, thus reducing the cost bound to be wasted on unnecessarily debugging test cases and production code by the same percentages (corresponding to reduction rates of 25% and 64%). Flakify also achieved significantly higher prediction results when used to predict test cases on new projects, suggesting better generalizability over FlakeFlagger. Our results further show that a black-box version of FlakeFlagger is not a viable option for predicting flaky test cases.

From Inheritance to Mockito: An Automatic Refactoring Approach

Unit testing focuses on verifying the functions of individual units of a software system. It is challenging due to the high inter dependencies among software units. Developers address this by mocking—replacing the dependency by a “fake” object. Despite the existence of powerful, dedicated mocking frameworks, developers often turn to a “hand-rolled” approach—inheritance. That is, they create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. This work contributes a fully automated refactoring framework to identify and replace the usage of inheritance by using Mockito—a well received mocking framework. Our approach is built upon the empirical experience from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">five</i> open source projects that use inheritance for mocking. We evaluate our approach on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nine</i> other projects. Results show that our framework is efficient, generally applicable to new datasets, mostly preserves test case behaviors in detecting defects (in the form of mutants), and decouples test code from production code. The qualitative evaluation by experienced developers suggests that the auto-refactoring solutions generated by our framework improve the quality of the unit test cases in various aspects, such as making test conditions more explicit, as well as improved cohesion, readability, understandability, and maintainability with test cases. Finally, we submit 23 pull requests containing our refactoring solutions to the open source projects. It turns our that, 9 requests are accepted/merged, 6 requests are rejected, the remaining requests are pending (5 requests), with unexpected exceptions (2 requests), or undecided (1 request). In particular, among the 21 open source developers that are involved in the reviewing process, 81% give positive votes. This indicates that our refactoring solutions are quite well received by the open source projects and developers.

Issue Information

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A Simple Stuck-at-faults Detection Method in Digital Combinational Circuits

This paper considers the new method of detection (diagnostic) stuck-at-faults (0/1) in digital combinational circuits based on a numerical set-theoretical approach. Compared to known methods and algorithms, the proposed approach differs in simpler implementation of searching for vectors of test codes at arbitrary points of the studied logic circuit. A few simple set-theoretical operations and procedures are sufficient to determine the location and the type of a stuck-at-fault (0/1). This is evidenced by the presented examples of application of the proposed method, that are borrowed from the publications of well-known authors.

InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessment

Automated test case generation has proven to be useful to reduce the usually high expenses of software testing. However, several studies have also noted the skepticism of testers regarding the comprehension of generated test suites when compared to manually designed ones. This fact suggests that involving testers in the test generation process could be helpful to increase their acceptance of automatically-produced test suites. In this paper, we propose incorporating interactive readability assessments made by a tester into EvoSuite, a widely-known evolutionary test generation tool. Our approach, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InterEvo-TR</small> , interacts with the tester at different moments during the search and shows different test cases covering the same coverage target for their subjective evaluation. The design of such an interactive approach involves a schedule of interaction, a method to diversify the selected targets, a plan to save and handle the readability values, and some mechanisms to customize the level of engagement in the revision, among other aspects. To analyze the potential and practicability of our proposal, we conduct a controlled experiment in which 39 participants, including academics, professional developers, and student collaborators, interact with <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">InterEvo-TR</small> . Our results show that the strategy to select and present intermediate results is effective for the purpose of readability assessment. Furthermore, the participants' actions and responses to a questionnaire allowed us to analyze the aspects influencing test code readability and the benefits and limitations of an interactive approach in the context of test case generation, paving the way for future developments based on interactivity.