scholarly journals Rotational Diversity in Multi-Cycle Assignment Problems

2019 ◽  
Vol 33 ◽  
pp. 7724-7731
Author(s):  
Helge Spieker ◽  
Arnaud Gotlieb ◽  
Morten Mossige
Keyword(s):  
Assignment Problem ◽  
Profit Maximization ◽  
Test Case ◽  
Test Cases ◽  
Assignment Problems ◽  
Assignment Model ◽  
Multiple Cycles ◽  
Multiple Knapsack ◽  
Objective Value

In multi-cycle assignment problems with rotational diversity, a set of tasks has to be repeatedly assigned to a set of agents. Over multiple cycles, the goal is to achieve a high diversity of assignments from tasks to agents. At the same time, the assignments’ profit has to be maximized in each cycle. Due to changing availability of tasks and agents, planning ahead is infeasible and each cycle is an independent assignment problem but influenced by previous choices. We approach the multi-cycle assignment problem as a two-part problem: Profit maximization and rotation are combined into one objective value, and then solved as a General Assignment Problem. Rotational diversity is maintained with a single execution of the costly assignment model. Our simple, yet effective method is applicable to different domains and applications. Experiments show the applicability on a multi-cycle variant of the multiple knapsack problem and a real-world case study on the test case selection and assignment problem, an example from the software engineering domain, where test cases have to be distributed over compatible test machines.

scholarly journals Using Fuzzy Logic in Test Case Prioritization for Regression Testing Programs with Assertions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ali M. Alakeel
Keyword(s):  
Fuzzy Logic ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Software Developers ◽  
Novel Approach ◽  
History Of ◽  
Supporting Tool

Program assertions have been recognized as a supporting tool during software development, testing, and maintenance. Therefore, software developers place assertions within their code in positions that are considered to be error prone or that have the potential to lead to a software crash or failure. Similar to any other software, programs with assertions must be maintained. Depending on the type of modification applied to the modified program, assertions also might have to undergo some modifications. New assertions may also be introduced in the new version of the program, while some assertions can be kept the same. This paper presents a novel approach for test case prioritization during regression testing of programs that have assertions using fuzzy logic. The main objective of this approach is to prioritize the test cases according to their estimated potential in violating a given program assertion. To develop the proposed approach, we utilize fuzzy logic techniques to estimate the effectiveness of a given test case in violating an assertion based on the history of the test cases in previous testing operations. We have conducted a case study in which the proposed approach is applied to various programs, and the results are promising compared to untreated and randomly ordered test cases.

Test Case Prioritization based on Early Fault-Detection Technique

2019 ◽  
Vol 12 ◽  
Author(s):  
Dharmveer Kumar Yadav ◽  
Sandip Dutta
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Object Oriented ◽  
Regression Testing ◽  
Object Oriented Programming ◽  
Test Case ◽  
Test Cases ◽  
Novel Approach ◽  
Fuzzy Logic Technique

egression testing is time consuming and expensive activity in software testing. In Regression testing when any changes made to already tested program it should not affect to other part of program. When some part of code is modified then it is necessary to validate the modified code. Throughout regression testing test case from test suite will be re-executed and re-execution of all the test case will be very expensive. We present fault based prioritization using fuzzy logic approach for object oriented software. We developed fuzzy expert model helps to takes better decision than other expert system for regression testing. Proposed work focus on concept of fault detection rate, execution time and coverage to select the test cases for prioritization purpose.We have taken case study and evaluated our work which shows proposed new framework gives better result than other approach. We present a novel approach for prioritization of test cases for object oriented programming using fuzzy logic technique during regression testing. We developed the proposed methodology, we apply fuzzy logic method for effective prioritization of test case. We have used case study of various programs, and the results are promising compared to other approach.

Test case prioritization and selection technique in continuous integration development environments: a case study

2018 ◽  
Vol 7 (2.28) ◽  
pp. 332 ◽  
Author(s):  
Lei Xiao ◽  
Huaikou Miao ◽  
Ying Zhong
Keyword(s):  
Execution Time ◽  
Regression Testing ◽  
Time Constraints ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Selection Technique ◽  
Continuous Integration ◽  
Study Results

Regression testing is a very important activity in continuous integration development environments. Software engineers frequently integrate new or changed code that involves in a new regression testing. Furthermore, regression testing in continuous integration development environments is together with tight time constraints. It is also impossible to re-run all the test cases in regression testing. Test case prioritization and selection technique are often used to render continuous integration processes more cost-effective. According to multi objective optimization, we present a test case prioritization and selection technique, TCPSCI, to satisfy time constraints and achieve testing goals in continuous integration development environments. Based on historical failure data, testing coverage code size and testing execution time, we order and select test cases. The test cases of the maximize code coverage, the shorter execution time and revealing the latest faults have the higher priority in the same change request. The case study results show that using TCPSCI has a higher cost-effectiveness comparing to the manually prioritization.  

scholarly journals Improving Fault Detection Rate using Similarity-based Test Case Prioritization in Regression Testing

2019 ◽  
Vol 9 (1) ◽  
pp. 1874-1880
Keyword(s):  
Rapid Evolution ◽  
Regression Testing ◽  
Maximum Efficiency ◽  
Test Case ◽  
Test Cases ◽  
Customer Requirements ◽  
Average Percentage ◽  
Software Developer ◽  
Complete Test

Rapid evolution in software requires regression testing to be performed as an essential activity which validates the software before the next release. Where software developer may add or removes intended features to maintain the software according to the customer requirements. In that case, complete test cases execution is nearly infeasible due to limited time and resources. So, the main aim of prioritization is to test any software with minimal time and maximum efficiency in terms of fault coverage rate. This paper proposed different similarity-based prioritization techniques to provide ranking to the test cases based on their influence level which is computed as similarity degree in three levels for the software to be tested. Each level represents the integration of selected coverage criteria’s. In order to validate our proposed technique, we have conducted a case study to measure its effectiveness in prioritizing the test cases. We experimentally observed that by incorporating a similarity-based approach with more than one coverage criteria; results for similarity-based prioritization are promising than any other conventional coverage based approaches in terms of Average Percentage of Faults Detected.

Evaluation Of Algorithm Performance For Simulated Square And Non-Square Logistic Assignment Problems

2021 ◽  
Author(s):  
Maximilian Selmair ◽  
Sascha Hamzehi ◽  
Klaus-Juergen Meier
Keyword(s):  
Approximation Method ◽  
Assignment Problem ◽  
Large Scale ◽  
Optimal Allocation ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Computational Time ◽  
Quadratic Assignment ◽  
Assignment Problems ◽  
Objective Value

The optimal allocation of transportation tasks to a fleet of vehicles, especially for large-scale systems of more than 20 Autonomous Mobile Robots (AMRs), remains a major challenge in logistics. Optimal in this context refers to two criteria: how close a result is to the best achievable objective value and the shortest possible computational time. Operations research has provided different methods that can be applied to solve this assignment problem. Our literature review has revealed six commonly applied methods to solve this problem. In this paper, we compared three optimal methods (Integer Linear Programming, Hungarian Method and the Jonker Volgenant Castanon algorithm) to three three heuristic methods (Greedy Search algorithm, Vogel’s Approximation Method and Vogel’s Approximation Method for non-quadratic Matrices). The latter group generally yield results faster, but were not developed to provide optimal results in terms of the optimal objective value. Every method was applied to 20.000 randomised samples of matrices, which differed in scale and configuration, in simulation experiments in order to determine the results’ proximity to the optimal solution as well as their computational time. The simulation results demonstrate that all methods vary in their time needed to solve the assignment problem scenarios as well as in the respective quality of the solution. Based on these results yielded by computing quadratic and non-quadratic matrices of different scales, we have concluded that the Jonker Volgenant Castanon algorithm is deemed to be the best method for solving quadratic and non-quadratic assignment problems with optimal precision. However, if performance in terms of computational time is prioritised for large non-quadratic matrices (50×300 and larger), the Vogel’s Approximation Method for non-quadratic Matrices generates faster approximated solutions.

scholarly journals Random Assignment Problems on 2d Manifolds

2021 ◽  
Vol 183 (2) ◽  
Author(s):  
D. Benedetto ◽  
E. Caglioti ◽  
S. Caracciolo ◽  
M. D’Achille ◽  
G. Sicuro ◽  
...  
Keyword(s):  
Assignment Problem ◽  
Unit Area ◽  
Constant Term ◽  
Beltrami Operator ◽  
Explicit Calculation ◽  
Dimensional Manifold ◽  
Two Dimensional ◽  
Random Assignment ◽  
Assignment Problems ◽  
Theoretical Formulation

AbstractWe consider the assignment problem between two sets of N random points on a smooth, two-dimensional manifold $$\Omega $$ Ω of unit area. It is known that the average cost scales as $$E_{\Omega }(N)\sim {1}/{2\pi }\ln N$$ E Ω ( N ) ∼ 1 / 2 π ln N with a correction that is at most of order $$\sqrt{\ln N\ln \ln N}$$ ln N ln ln N . In this paper, we show that, within the linearization approximation of the field-theoretical formulation of the problem, the first $$\Omega $$ Ω -dependent correction is on the constant term, and can be exactly computed from the spectrum of the Laplace–Beltrami operator on $$\Omega $$ Ω . We perform the explicit calculation of this constant for various families of surfaces, and compare our predictions with extensive numerics.

scholarly journals Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 152
Author(s):  
Micha Zoutendijk ◽  
Mihaela Mitici
Keyword(s):  
Assignment Problem ◽  
Absolute Error ◽  
Aviation Industry ◽  
Probabilistic Forecasting ◽  
Flight Delays ◽  
Mixture Density ◽  
Assignment Model ◽  
Gate Assignment ◽  
Flight Delay ◽  
Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

scholarly journals Resource and dependency based test case generation for RESTful Web services

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Man Zhang ◽  
Bogdan Marculescu ◽  
Andrea Arcuri
Keyword(s):  
Web Services ◽  
Domain Knowledge ◽  
Automated System ◽  
Test Case ◽  
Test Cases ◽  
Evolutionary Search ◽  
Sampling Strategies ◽  
Mutation Operators ◽  
On Line ◽  
Restful Web Services

AbstractNowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.

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