Regression Testing Techniques Research Articles

PENGARUH FEAR OF MISSING OUT TERHADAP PERILAKU PHUBBING PADA REMAJA PENGGUNA MEDIA SOSIAL

The development of increasingly advanced technological times and the presence of the internet in Indonesia has reached hundreds of millions of users who use it for various purposes, one of which is for accessing social media, which reaches more than ninety percent, of which Indonesian teenagers in the 13-18 year age group use it the most. The aim of this research is to determine the influence of fear of missing out on phubbing behavior among adolescent social media users. The sampling techinique uses purposive sampling, the sample is 158 teenage respondents who use social media. The phubbing behavior scale compiled is based on aspects: communication disturbance and phone obsession, the fear of missing out scale compiled in this research is based on aspects: self and relatedness. This research uses a simple regression testing technique using SPSS version 25 for MacOS. The results of this research are that the hypothesis is accepted with a significance value of 0.000 (p<0.05), namely that fear of missing out has a very significant effect on phubbing behavior in adolescent social media users.

Pengaruh Self-Compassion terhadap Korban Bullying Pada Siswa SMA Negeri 14 Semarang

The aim of this research was to determine the effect of self-compassion on bullying victims among students at SMA Negeri 14 Semarang. This research uses a quantitative ex post facto design approach. The population in this study was 287 students. The research sample consisted of 287 class X students taken using probability sampling techniques. The data collection tools used were the self-compassion scale and the bullying victim scale. The validity test results of the self-compassion scale are in the range 0.176 – 0.776 and the bullying victim scale is in the range 0.170 – 0.760. The reliability test results of the self-compassion scale had a Cronbach's alpha value of 0.944 and the bullying victim scale had a Cronbach's alpha value of 0.909, both of which were included in the strong category. Based on these results, both scales can be said to be valid and reliable. This research uses multiple regression testing techniques as a data analysis technique. The self-compassion variable is in the medium category (M = 81,25; SD = 9,10) and the bullying victim variable is in the medium category (M = 100,33; SD = 9,51). The results of this study show that self-compassion has an effect on victims of bullying among students at SMA Negeri 14 Semarang. Self-compassion explains that it has an influence of 33,6% (R2 =.336). The results of this research have implications for guidance and counseling in carrying out prevention and alleviation functions for students who are victims of bullying and have traumatic impacts from acts of bullying behavior at school.

PENGARUH KUALITAS PELAYANAN BENGKEL TERHADAP LOYALITAS PELANGGAN MELALUI KEPUASAN PELANGGAN

This research was conducted at PT Setiajaya Mobilindo - Setiajaya Toyota Depok, with the aim of Knowing the influence of workshop service quality on customer satisfaction Knowing the influence of customer satisfaction on customer loyalty and Knowing the level of quality of workshop service provided on customer loyalty through PT Setiajaya Mobilindo - Setiajaya Toyota Depok customer satisfaction. The population in this study were all customers at PT Setiajaya Mobilindo - Setiajaya Toyota Depok Pekanbaru. Using the Slovin formula and accidental sampling technique, 161 selected samples were obtained. The research method was carried out descriptively and quantitatively, using simple regression testing techniques and the data was then processed using the IBM SPSS Statistics 26 program. With this research, research estimates were produced as to whether service quality has an effect on customer satisfaction and also whether customer satisfaction and service quality have an impact. influence on customer loyalty.

Comparative Analysis for Test Case Prioritization Using Particle Swarm Optimization and Firefly Algorithm

Software testing is the most importance phases for software development life cycle. However, it is always time consuming and costly. In order to solve this problem, regression testing is required to be conducted since it can verify the software modifications with zero effect to the software actual features. TCP is one type of the regression testing techniques. It can reduce the cost and time taken. In the area of TCP, there are several algorithms and the most powerful algorithms is the nature inspired algorithms. This study will focus on the comparison analysis of prioritization of test case by using PSO and FA. In order to choose an algorithm with better performance between PSO and FA, they are turned into the form of Python code. Then, PSO and FA are implemented into Case Study A and Case Study B. Their result will be compared and analysis based on the execution time, Big-O, and APFD. The comparison showed that FA is outperform than PSO since FA has the least execution time (0.001 second), less complexity (O(N)) than PSO (O(N3)), and same APFD values (0.520 and 0.600). Thus, FA has better prioritization performance compared to PSO.

Using goal–question–metric to compare research and practice perspectives on regression testing

AbstractRegression testing is challenging because of its complexity and the amount of effort and time it requires, especially in large‐scale environments with continuous integration and delivery. Regression test selection and prioritization techniques have been proposed in the literature to address the regression testing challenges, but adoption rates of these techniques in industry are not encouraging. One of the possible reasons could be the disparity in the regression testing goals in industry and literature. This work compares the research perspective to industry practice on regression testing goals, corresponding information needs, and metrics required to evaluate these goals. We have conducted a literature review of 44 research papers and a survey with 56 testing practitioners. The survey comprises 11 interviews and 45 responses to an online questionnaire. We identified that industry and research accentuate different regression testing goals. For instance, the literature emphasizes increasing the fault detection rates of test suites and early identification of critical faults. In contrast, the practitioners' focus is on test suite maintenance, controlled fault slippage, and awareness of changes. Similarly, the literature suggests maintaining information needs from test case execution histories to evaluate regression testing techniques based on various metrics, whereas, at large, the practitioners do not use the metrics suggested in the literature. To bridge the research and practice gap, based on the literature and survey findings, we have created a goal–question–metric (GQM) model that maps the regression testing goals, associated information needs, and metrics from both perspectives. The GQM model can guide researchers in proposing new techniques closer to industry contexts. Practitioners can benefit from information needs and metrics presented in the literature and can use GQM as a tool to follow their regression testing goals.

Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization.

The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.

Peran Return on Asset dalam Memoderasi Hubungan Biaya Operasional Pendapatan Operasional, BI Rate, dan Capital Adequacy Ratio Terhadap Persentase Bagi Hasil Deposito Mudharabah Pada Bank Umum Syariah di Indonesia

This study to determine the role of Return On Assets (ROA) in the relationship between Operational Costs from Operating Income (BOPO), BI Rate, and Capital Adequacy Ratio (CAR) to the percentage of profit sharing on mudharabah deposits at Islamic Commercial Banks in Indonesia at 2015-2019 period. This study uses field research with a quantitative approach to Islamic commercial banks registered with the Financial Services Authority as the population in the form of panel data. This study uses multiple linear regression testing techniques. The results showed that the BOPO and BI Rate showed that this variable had no significant positive effect on the percentage of profit sharing for mudharabah deposits. CAR shows that this variable has a negative and significant effect on the percentage of profit sharing for mudharabah deposits. Based on the Moderated Regression Analysis (MRA) test, the results show that ROA can moderate the relationship between the BOPO variable and the percentage of profit sharing for mudharabah deposits. ROA cannot moderate the relationship between the BI Rate variable and the CAR variable on the percentage of profit sharing for mudharabah deposits.

Applying test case prioritization to software microbenchmarks

Regression testing comprises techniques which are applied during software evolution to uncover faults effectively and efficiently. While regression testing is widely studied for functional tests, performance regression testing, e.g., with software microbenchmarks, is hardly investigated. Applying test case prioritization (TCP), a regression testing technique, to software microbenchmarks may help capturing large performance regressions sooner upon new versions. This may especially be beneficial for microbenchmark suites, because they take considerably longer to execute than unit test suites. However, it is unclear whether traditional unit testing TCP techniques work equally well for software microbenchmarks. In this paper, we empirically study coverage-based TCP techniques, employing total and additional greedy strategies, applied to software microbenchmarks along multiple parameterization dimensions, leading to 54 unique technique instantiations. We find that TCP techniques have a mean APFD-P (average percentage of fault-detection on performance) effectiveness between 0.54 and 0.71 and are able to capture the three largest performance changes after executing 29% to 66% of the whole microbenchmark suite. Our efficiency analysis reveals that the runtime overhead of TCP varies considerably depending on the exact parameterization. The most effective technique has an overhead of 11% of the total microbenchmark suite execution time, making TCP a viable option for performance regression testing. The results demonstrate that the total strategy is superior to the additional strategy. Finally, dynamic-coverage techniques should be favored over static-coverage techniques due to their acceptable analysis overhead; however, in settings where the time for prioritzation is limited, static-coverage techniques provide an attractive alternative.

Simulation and Regression Testing Technique for Software Formal Specifications Based on Extended Place/Transition Net with Attributed Tokens

We propose a technique of simulation and regression testing for Extended Place/transition Net with Attributed Tokens (EPNAT) models, and then show a prototype tool to partially support it. In the technique, the information about a current marking, current values of variables, and current fireable transitions is indicated to assist engineers in finding faults and selecting next transitions to be fired for the simulation. Also, good execution traces in the simulation are recorded as test cases for the regression testing. When an EPNAT model is modified, the test cases can be applied to it in order to reveal regression failures. A preliminary experiment using simple software requirements has been carried out to discuss the effectiveness of the proposed technique.

Discrete cuckoo search algorithms for test case prioritization

Regression testing is an essential aspect of the software development lifecycle. As the software evolves, the test suite grows, hence the cost and effort to retest the software. Test case prioritization is one of the mitigation techniques for regression testing. It ranks the test cases to maximize the desired properties, e.g., detecting faults early. The efficiency and effectiveness of test case prioritization techniques can be enhanced using optimization algorithms. Nature-inspired algorithms are gaining more attention due to their easy implementation and quality of the solutions. This paper proposes the discrete cuckoo search algorithm for test case prioritization. The prioritization problem deals with ordering the test cases. Therefore, a new adaptation strategy using asexual genetic reproduction is introduced to convert real numbers into permutation sequences. Furthermore, the cuckoo search algorithm’s effectiveness is extended with the genetic algorithm’s mutation operator to balance the trade-off between exploration and exploitation. An in-depth comparative study on four case studies is conducted between the proposed algorithms, existing state-of-the-art algorithms and baseline approach. Statistical investigation confirms that the proposed hybrid cuckoo search algorithm outperforms the genetic algorithm, particle swarm optimization, ant colony optimization, tree seed algorithm and random search by 4.29%, 5.52%, 8.38%, 2.74% and 10.80%, respectively.

Regression Test Case Prioritization: A Systematic Literature Review

The techniques associated with the Test Case Prioritization (TCP) are used to reduce the cost of regression testing to achieve the objectives that the modifications in the target code would not impact the functionality of updated software. The effectiveness of the TCP is measured based on the cost, the code coverage, and fault detection ability. The regression testing techniques proposed so far are focusing on one or two effectiveness parameters. In this paper, we presented a state-of-art review of the approaches used in regression testing in detail. The second objective is to combine these effective adequacy measures into a single or multi-objective TCP task. This systematic literature review is conducted to identify the state-of-the-art research in regression TCP from 2007 to 2020. The research identifies fifty-two (52) relevant studies that were focusing on these three selection parameters to justify their findings. The results reveal that there were six families of regression TCP in which meta-heuristic regression TCP were reported in 38% and generic regression TCP techniques in 31%. The parameters used as prioritization criteria were cost, code coverage, and fault detection ability. The code coverage is reported by 38%, cost in 17%, and cost and code coverage in 31%. There were three sources for datasets were identified named Software artefact Infrastructure Repository (SIR), Apache Software Foundation, and Git Hub. The measurement and metrics used to validate the effectiveness are inclusiveness, precision, recall, and retest-all.

STUDI ALGORITMA babelRTS UNTUK PELAKSANAAN REGRESSION TEST SELECTION (RST)

Software testing (testing) is a crucial stage in software development. The success of the testing process will ensure the quality of the software. In the regression testing process, one issue is that not all test cases (retest all) in the test suite need to be executed. Retest all will consume massive resources, as well as a long time. Regression testing techniques seek to find ways to reduce test execution time. One of the regression testing techniques is test case selection, also known as regression test selection (RTS). This paper describes a study on babelRTS, an RTS algorithm, to see its effectiveness. Effectiveness is measured by comparing the execution time of the execution retest all and babelRTS. Experiments were carried out on five software under tests (SUT) that had some faults. Test cases are prepared by designing for each SUT. The results showed a reduction in time so that the effectiveness reached a maximum of 32%, and average of 23% .

Regression testing for large-scale embedded software development – Exploring the state of practice

Context: A majority of the regression testing techniques proposed by the research have not been adopted in industry. To increase adoption rates, we need to better understand the practitioners' pers ...

Enhanced regression testing technique for agile software development and continuous integration strategies

To survive in competitive marketplaces, most organizations have adopted agile methodologies to facilitate continuous integration and faster application delivery and rely on regression testing during application development to validate the quality and reliability of the software after changes have been made. Consequently, for large projects with cost and time constraints, it is extremely difficult to determine which test cases to run at the end of each release. In this paper, a test case prioritization and selection approach is proposed to improve the quality of releases. From existing literature, we analyzed prevailing problems and proposed solution relevant to regression testing in agile practices. The proposed approach is based on two phases. First, test cases are prioritized by clustering those test cases that frequently change. In case of a tie, test cases are prioritized based on their respective failure frequencies and coverage criteria. Second, test cases with a higher frequency of failure or coverage criteria are selected. The proposed technique was validated by an empirical study on three industrial subjects. The results show that the method successfully selects an optimal test suite and increases the fault detection rate (i.e., more than 90% in the case of proposed technique and less than 50% in other techniques), which reduces the number of irrelevant test cases and avoids detecting duplicate faults. The results of evaluation metrics illustrate that the proposed technique significantly outperform (i.e., between 91 and 97%) as compared to other existing regression testing techniques (i.e., between 52 and 68%). Therefore, our model enhances the test case prioritization and selection with the ability for earlier and high fault detection. Thus, pruning out irrelevant test cases and redundant faults and enhancing the regression testing process for agile applications.

Ant Colony System With Sorting-Based Local Search for Coverage-Based Test Case Prioritization

Test case prioritization (TCP) is a popular regression testing technique in software engineering field. The task of TCP is to schedule the execution order of test cases so that certain objective (e.g., code coverage) can be achieved quickly. In this article, we propose an efficient ant colony system framework for the TCP problem, with the aim of maximizing the code coverage as soon as possible. In the proposed framework, an effective heuristic function is proposed to guide the ants to construct solutions based on additional statement coverage among remaining test cases. Besides, a sorting-based local search mechanism is proposed to further accelerate the convergence speed of the algorithm. Experimental results on different benchmark problems, and a real-world application, have shown that the proposed framework can outperform several state-of-the-art methods, in terms of solution quality and search efficiency.

On the use of usage patterns from telemetry data for test case prioritization

Abstract Context Modern applications contain pervasive telemetry to ensure reliability and enable monitoring and diagnosis. This presents a new opportunity in the area of regression testing techniques, as we now have the ability to consider usage profiles of the software when making decisions on test execution. Objective The results of our prior work on test prioritization using telemetry data showed improvement rate on test suite reduction, and test execution time. The objective of this paper is to further investigate this approach and apply prioritization based on multiple prioritization algorithms in an enterprise level cloud application as well as open source projects. We aim to provide an effective prioritization scheme that practitioners can implement with minimum effort. The other objective is to compare the results and the benefits of this technique factors with code coverage-based prioritization approaches, which is the most commonly used test prioritization technique. Method We introduce a method for identifying usage patterns based on telemetry, which we refer to as “telemetry fingerprinting.” Through the use of various algorithms to compute fingerprints, we conduct empirical studies on multiple software products to show that telemetry fingerprinting can be used to more effectively prioritize regression tests. Results Our experimental results show that the proposed techniques were able to reduce over 30% in regression test suite run times compared to the coverage-based prioritization technique in detecting discoverable faults. Further, the results indicate that fingerprints are effective in identifying usage patterns, and that the fingerprints can be applied to improve regression testing techniques. Conclusion In this research, we introduce the concept of fingerprinting software usage patterns through telemetry. We provide various algorithms to compute fingerprints and conduct empirical studies that show that fingerprints are effective in identifying distinct usage patterns. By applying these techniques, we believe that regression testing techniques can be improved beyond the current state-of-the-art, yielding additional cost and quality benefits.

On the search for industry-relevant regression testing research

Regression testing is a means to assure that a change in the software, or its execution environment, does not introduce new defects. It involves the expensive undertaking of rerunning test cases. Several techniques have been proposed to reduce the number of test cases to execute in regression testing, however, there is no research on how to assess industrial relevance and applicability of such techniques. We conducted a systematic literature review with the following two goals: firstly, to enable researchers to design and present regression testing research with a focus on industrial relevance and applicability and secondly, to facilitate the industrial adoption of such research by addressing the attributes of concern from the practitioners’ perspective. Using a reference-based search approach, we identified 1068 papers on regression testing. We then reduced the scope to only include papers with explicit discussions about relevance and applicability (i.e. mainly studies involving industrial stakeholders). Uniquely in this literature review, practitioners were consulted at several steps to increase the likelihood of achieving our aim of identifying factors important for relevance and applicability. We have summarised the results of these consultations and an analysis of the literature in three taxonomies, which capture aspects of industrial-relevance regarding the regression testing techniques. Based on these taxonomies, we mapped 38 papers reporting the evaluation of 26 regression testing techniques in industrial settings.

A Systematic Literature Review of Test Case Prioritization Using Genetic Algorithms

Regression testing is the essential process of software maintenance and evolution phase of the software development life cycle for assuring the quality and reliability of updated software. Test case prioritization is the technique of regression testing to reduce the time and effort required for regression testing. Search-based algorithms are used to enhance the efficiency and effectiveness of the method. Among these search-based optimization algorithms, genetic algorithms are becoming more popular among researchers since the last decade. In this paper, we are doing a systematic literature review, i.e., a secondary study of test case prioritization using genetic algorithms. The objective of this review is to examine and classify the current state of use of the genetic algorithm in test case prioritization. In other words, to give a base for the advancement of test case prioritization research using genetic algorithms. With the use of the systematic literature review protocol, we selected the most relevant studies (20 out of 384) from the appropriate repositories by using a set of search keywords, inclusion/exclusion criteria and the quality assessment of studies. The data extraction and synthesis process and the taxonomic classification are used to answer the research questions. We also performed a rigorous analysis of the techniques by comparing them on research methodology, the prioritization method, dataset specification, test suite size, types of genetic algorithms used, performance metrics, and the validation criteria. The whole process took four months for comprehensive analysis and classification of primary studies. We observed that the parameter settings, the type of operators, the probabilistic rate of operators, and fitness function design have a significant impact on the quality of the solutions obtained. This systematic literature review yields that genetic algorithms have great potential in solving test case prioritization problems, and the area is open for further improvements. Future researchers can fill the research gaps by following the suggestions given in the review. From this review, we found that the use of the appropriate approach can make a genetic algorithm based test case prioritization one of the effective methods in regression testing.

지속적 통합 환경에서 회귀 테스트 기술의 비용효율성에 대한 실패 관심도 기반 분석

지속적 통합 환경에서 회귀 테스트 기술의 비용효율성에 대한 실패 관심도 기반 분석

How Do Static and Dynamic Test Case Prioritization Techniques Perform on Modern Software Systems? An Extensive Study on GitHub Projects

Test Case Prioritization (TCP) is an increasingly important regression testing technique for reordering test cases according to a pre-defined goal, particularly as agile practices gain adoption. To better understand these techniques, we perform the first extensive study aimed at empirically evaluating four static TCP techniques, comparing them with state-of-research dynamic TCP techniques across several quality metrics. This study was performed on 58 real-word Java programs encompassing 714 KLoC and results in several notable observations. First, our results across two effectiveness metrics (the Average Percentage of Faults Detected APFD and the cost cognizant APFDc) illustrate that at test-class granularity, these metrics tend to correlate, but this correlation does not hold at test-method granularity. Second, our analysis shows that static techniques can be surprisingly effective, particularly when measured by APFDc. Third, we found that TCP techniques tend to perform better on larger programs, but that program size does not affect comparative performance measures between techniques. Fourth, software evolution does not significantly impact comparative performance results between TCP techniques. Fifth, neither the number nor type of mutants utilized dramatically impact measures of TCP effectiveness under typical experimental settings. Finally, our similarity analysis illustrates that highly prioritized test cases tend to uncover dissimilar faults.