Parallel Test Prioritization

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing. Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization. To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from Baidu , a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.

Use of Software Metrics to Improve the Quality of Software Projects Using Regression Testing

Testing of software is broadly divided into three types i.e., code based, model based and specification based. To find faults at early stage, model based testing can be used in which testing can be started from design phase. Furthermore, in this chapter, to generate new test cases and to ensure the quality of changed software, regression testing is used. Early detection of faults will not only reduce the cost, time and effort of developers but also will help finding risks. We are using structural metrics to check the effect of changes made to software. Finally, the authors suggest identifying metrics and analyze the results using NDepend simulator. If results show deviation from standards then again perform regression testing to improve the quality of software.

Faktor yang Mempengaruhi Minat Sarjana Pertanian Fakultas Pertanian UNS untuk Bekerja di Bidang Pertanian

<em>This study aims to (1) determine the interest of graduates of the Faculty of Agriculture Universitas Sebelas Maret (UNS) Surakarta to work in the agricultural sector, (2) to examine the factors that influence the interest of graduates of the Faculty of Agriculture UNS to work in the agricultural sector, (3) to examine how the influence of factors that influence the interest of graduates of the Faculty of Agriculture UNS to work in the agricultural sector</em>.<em> This research was carried out at the Faculty of Agriculture, UNS Surakarta. The method used in this study is a quantitative research method with survey techniques. The population in this study were alumni of the faculty of agriculture who graduated in 2019. The data processing was carried out by researchers using the classical assumption test, which consisted of normality test, linearity test, multicollinearity test and heteroscedasticity test. After completing the classical assumption test, the data analysis was continued with regression testing, F-test and T-test. The results showed that: (1) the interest of UNS agricultural graduates to work in agriculture was mostly in the high and sufficient category. (2) The perception factor of work in agriculture, experience, lifestyle, family environment and the closest social environment simultaneously have a significant influence on the interest of graduates of the Faculty of Agriculture UNS to work in the agricultural sector. (3) The perception factor of work in agriculture, lifestyle, family environment and the closest social environment partially has a significant influence on the interest of graduates of the Faculty of Agriculture UNS to work in the agricultural sector, while the experience factor partially does not have a significant influence.</em>

Estimation Approaches of Machine Learning in Scrum Projects: A Review

It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx 43% of the projects are often delivered late and entered crises because of over budget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g. Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon forstrengthening the collaboration betweendeveloper and customer.Estimation has always been challenging in Agile as requirements are volatile. This encourages researchersto work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paperreviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation. Keywords: Machine Learning, Scrum, Scrum Projects, Effort Estimation, Agile Software Development

KUALITAS PRODUK MENENTUKAN KEPUTUSAN PEMBELIAN TAGAR STUDI KASUS PADA KONSUMEN TAGAR BANDUNG CABANG PASIRKALIKI

Tofu is a food made from soybeans and is closely related to Indonesian food. Businesses in the tofu sector, of course, need high innovation and the right promotion so that consumers can be interested in buying tofu. The purpose of this research is to determine whether product quality determines purchasing decisions. The method used in this research is descriptive verification with a quantitative approach through a survey. Based on the Slovin formula, the number of respondents studied was 98 people. The data analysis used multiple linear regression after the questionnaire result data was made in the form of MSI and the classical assumption test was fulfilled. The hypothesis of this study is that product quality and promotion have a positive effect partially or simultaneously to the purchase decision. The results of regression testing show that there is a positive effect either simultaneously or partially of Product Quality and Promotion on Purchasing Decisions.

CIPC: A Change Impact Propagation Computing Based Technique for Microservice Regression Testing Prioritization

Regression testing is the optimal technique that can be used in each iteration of microservice systems. However, regression testing prioritization is the only main method that gives better results. These techniques directly involve the processes of artifacts, data acquisition, analysis, and maintenance. The microservice systems have input data, which are difficult to obtain and control, while such processes are of high costs with impractical design. This paper gives a detailed study on testing prioritization technique, which is referred to as CIPC. As there are dependencies between services from API gateway logs, a novel CIPC algorithm is proposed, which is based on belief propagation. There are some rules that are directly affected by service changes. Therefore, the higher execution order of test case prioritizes CIPC, which is based on impact changes. Multiobjective prioritization algorithm is based on heuristic searching, in which sequence test cases are done by coverage. By evaluating the effectiveness of CIPC, the empirical study presents five microservice systems and four different techniques. The results describe that CIPC has improved fault detection rate with acceptable time and cost. The technique is more practical than typical artifacts, which are based on increments of system scales.

TAX AVOIDANCE PADA PERUSAHAAN MANUFAKTUR INDONESIA

A complicated tax problem in Indonesia is tax avoidance. Tax avoidance is a legal action that is detrimental to the state. This research aims to determine whether tax avoidance affects leverage, firm size and return on assets. This study was tested using multiple linear regression with the dependent variable tax avoidance and independent variables in the form of leverage, firm size, and return on assets. The population in this research is companies that listed in Indonesian Stck Exchanged (www.idx.co.id) and sample in this study amounted to 50 (fifty) manufacturing companies. The selection of the research sample was done by random sampling. Multiple regression testing uses SPSS 25 software. The results show that leverage has a positive effect on tax avoidance, while firm size and return on assets have no effect on tax avoidance. Companies with high debt levels tend to do tax avoidance. On the other hand, companies of any size and any return on assets can carry out tax avoidance

The Estimation of Iron Oxide Content in Soil based on Landsat 8 OLI TIRS Imagery in Wetland Areas

Wetland areas are volatile and have high iron content. In this study, through a remote sensing approach, especially using Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery, we discussed the method to estimate the presence of iron oxide in the wetlands of South Kalimantan in 2018, 2019, and 2020. Interpretation of the Landsat OLI TIRS was employed in April 2018, August 2018, February 2019, August 2019, March 2020, and August 2020. The band ratio method was used to determine the distribution of samples in this study. The results of the iron oxide index from the image were performed regression and correlation analysis with field measurement and laboratory test results to validate the oxide index values. The results showed that the iron oxide index value in the dry season was higher than in the rainy season. Iron oxide index value in open land was higher than in vegetation cover. The wetland was in dry condition during the dry season, making it easier to detect iron oxide values. Vegetation cover could reduce the iron oxide index value on the soil surface so that the iron oxide value was more easily identified in open land. The results of linear regression testing for the wet season sample obtained a coefficient of determination R² = 0.413, while the results of linear regression testing for the dry season sample obtained a coefficient of determination R² = 0.667. Thus, the Landsat image has strong enough to estimate the iron oxide index in the wetland area of Kalimantan.

Ontology-Based Regression Testing: A Systematic Literature Review

Web systems evolve by adding new functionalities or modifying them to meet users’ requirements. Web systems require retesting to ensure that existing functionalities are according to users’ expectations. Retesting a web system is challenging due to high cost and time consumption. Existing ‘systematic literature review’ (SLR) studies do not comprehensively present the ontology-based regression testing approaches. Therefore, this study focuses on ontology-based regression testing approaches because ontologies have been a growing research solution in regression testing. Following this, a systematic search of studies was performed using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines. A total of 24 peer-reviewed studies covering ontologies (semantic and inference rules) and regression testing, published between 2007 and 2019, were selected. The results showed that mainly ontology-based regression testing approaches were published in 2011–2012 and 2019 because ontology got momentum in research in other fields of study during these years. Furthermore, seven challenges to ontology-driven regression testing approaches are reported in the selected studies. Cost and validation are the main challenges examined in the research studies. The scalability of regression testing approaches has been identified as a common problem for ontology-based and other benchmark regression testing approaches. This SLR presents that the safety of critical systems is a possible future research direction to prevent human life risks.