Optimize SPL test cases with adaptive simulated annealing genetic algorithm

Orthogonal variable spreading factor (OVSF) codes are widely used to provide variable data rates for supporting different bandwidth requirements in wideband code division multiple access (WCDMA) systems. Many works in the literature have intensively investigated to find an optimal dynamic code assignment scheme for OVSF codes. Unlike earlier studies, which assign OVSF codes using conventional (CCA) or dynamic (DCA) code allocation schemes, in this paper, adaptive simulated annealing genetic algorithm (ASAGA) was applied which population is adaptively constructed according to existing traffic density in the OVSF code-tree. Also, the influences of the ASAGA parameters (selection, crossover and mutation techniques and cooling schedules) were examined on the dynamic OVSF code allocation problem. The simulation results show that the ASAGA provides reduced code blocking probability and improved spectral efficiency in the system when compared to the CCA and DCA schemes. ASAGA is also tested with its components SA and GA.

To find the best polarity of large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits, this paper proposes a new Adaptive Simulated Annealing Genetic Algorithm (ASAGA) which can effectively find out the best polarity. Genetic Algorithm (GA) has outstanding global searching ability but easily falls into the local optimum, while the Simulated Annealing Algorithm (SAA) is expert in local searching but has the limitation of poor convergence. The new method incorporating SAA into GA is proposed which can play their strengths to the utmost extent and makes up their shortcomings. Additionally, for the key parameters of ASAGA, we adopt self-adaptive adjustment, which can effectively improve the convergence and robustness of the algorithm. The results proved the efficiency and accuracy of ASAGA in searching the best polarity of large-scale MPRM logic circuits.

In many cases, Software Product Line Testing (SPLT) targets only the selection of test cases which cover product features or feature interactions. However, higher testing efficiency can be achieved through the selection of test cases with improved fault-revealing capabilities. By associating each test case a priority-value representing (or aggregating) different criteria, such as importance (in terms of fault discovered in previous test campaigns), duration or cost, it becomes possible to select a feature-covering test suite with improved capabilities. A crucial objective in SPLT then becomes to identify a test suite that optimizes reaching a specific goal (lower test duration or cost), while preserving full feature coverage.

As a distributed energy storage system, ice-storage air conditioning system can not only reduce the cost and improve the efficiency of the existing power system but it can also plays an important role in the demand side management. But how to get the optimal allocation proportion of cooling load between ice storage and chillers still is an unsolved problem. A nonlinear programming is constructed based on the improved model of facilities to achieve the optimization of the ice-storage air conditioning system. Then, an adaptive simulated annealing genetic algorithm (ASAGA) is proposed to solve this nonlinear problem. Finally, the effectiveness of the given facility models and nonlinear program as well as ASAGA are tested by a practical project analysis.

As a result of the emergence of many kinds of high-speed communication systems and increasing demand of distributed multimedia applications, efficient and effective support of quality of service (QoS) has become more and more essential. Many service models and mechanisms have been put forward by IETF to meeting QoS requirement, multicast service is a key one of them and is becoming a key requirement of computer network supposing multimedia application.Multicasting consists of concurrently sending the same information from a source to a subset of all possible destinations in a computer network. Multicast utilizes a tree delivery structure, on which data packets are duplicated only at fork nodes and are forwarded only once over each link. This approach makes multicast resource—efficient in delivering data to a group of member simultaneously and can scale well to support very large multicast group. This paper focuses on the algorithms to construct low cost multicast routing tree with QoS requirements.In this paper, we study the bandwidth, delay, delay jitter, and packet loss constrained least cost multicast routing problem which is known to be NP-complete, and present a adaptive genetic simulated annealing algorithm (AGASA) to solve the problem. The proposed genetic algorithm has the following characteristics: (1) Tree structure coding scheme, by which we can use any data structure of the tree to describe the chromosome structure, is an effective solution to the algorithm in the coding and decoding. (2) Genetic algorithm is improved, and simulated annealing algorithm is combined with. Simulated annealing algorithm has stronger local search capabilities, which avoids a partial optimum during the search process. (3) The crossover and mutation probability of adaptive genetic algorithm is improved. The selection of the crossover and mutation probability is the key to the behavior and performance of the algorithm, which has the direct impact on the convergence of the algorithm.The simulation results show that the algorithm has fast convergence and better performance. It is able to satisfy the changing network scale.

Software Product Line (SPL) is a popular research area in software engineering which deals with many products generated simultaneously. There is a requirement for a tool or framework that can test all the products. SPL implies Test Case Prioritisation (TCP) and Test Case Reduction (TCR) techniques to alleviate the problem of testing. This paper proposes a hybrid approach which combines K-Means and Principal Component Analysis (PCA) approaches to perform SPL testing. The experimental results show that the hybrid approach-based order provides better results than the random, similarity, ICPL algorithm-based order using APFD metric. Our proposed approach reduces the test cases to the minimum amount and is also used to perform efficient SPL testing that not only improves the effectiveness and efficiency of fault detection but also improves the effectiveness of Test Suite Reduction (TSR).

Improving the effectiveness of test suite reduction for user-session-based testing of web applications

Software Testing is an important aspect of the real time software development process. Software testing always assures the quality of software product. As associated with software testing, there are few very important issues where there is a need to pay attention on it in the process of software development test. These issues are generation of effective test case and test suite as well as optimization of test case and suite while doing testing of software product. The important issue is that testing time of the test case and test suite. It is very much important that after development of software product effective testing should be performed. So to overcome these issues of optimization, we have proposed new approach for test suite optimization using genetic algorithm (GA). Genetic algorithm is evolutionary in nature so it is often used for optimization of problem by researcher. In this paper, our aim is to study various selections methods like tournament selection, rank selection and roulette wheel selection and then we apply this genetic algorithm (GA) on various programs which will generate optimized test suite with parameters like fitness value of test case, test suite and take minimum amount of time for execution after certain preset generation. In this paper our main objectives as per the experimental investigation, we show that tournament selection works very fine as compared to other methods with respect fitness selection of test case and test suites, testing time of test case and test suites as well as number of requirements.

Software product line (SPL) testing consists of two separate but closely related test engineering activities: domain testing and application testing. Various software product line testing approaches have been developed over the last decade, and surveys have been conducted on them. However, thus far none of them deeply addressed the questions of what researches have been conducted in order to overcome the challenges posed by the two separate testing activities and their relationships. Thus, this paper surveys the current software product line testing approaches by defining a reference SPL testing processes and identifying, based on them, key research perspectives that are important in SPL testing. Through this survey, we identify the researches that addressed the challenges and also derive open research opportunities from each perspective.

Software testing is a critical part of software development. As new test cases are generated over time due to software modifications, test suite sizes may grow significantly. Because of time and resource constraints for testing, test suite minimization techniques are needed to remove those test cases from a suite that, due to code modifications over time, have become redundant with respect to the coverage of testing requirements for which they were generated. Prior work has shown that test suite minimization with respect to a given testing criterion can significantly diminish the fault detection effectiveness (FDE) of suites. We present a new approach for test suite reduction that attempts to use additional coverage information of test cases to selectively keep some additional test cases in the reduced suites that are redundant with respect to the testing criteria used for suite minimization, with the goal of improving the FDE retention of the reduced suites. We implemented our approach by modifying an existing heuristic for test suite minimization. Our experiments show that our approach can significantly improve the FDE of reduced test suites without severely affecting the extent of suite size reduction

The proportion of railway cold chain transportation in the overall cold chain logistics transportation market is relatively small in China. Freight subsidies and cold chain train operations are typically effective approaches to guide the public transit of cold chain cargo flow and grow the railway cold chain transportation market. We analyzed the cost structure of a cold chain transportation network. We established a network optimization model of the railway cold chain logistics based on a freight subsidy and designed an adaptive genetic–simulated annealing algorithm (A-SAGA). Taking the cold chain transportation between the Yangtze river delta urban agglomerations and the Chengdu–Chongqing city group as an example, we determined the optimal cold chain logistics transportation scheme using the traditional genetic algorithm and the A-SAGA. Moreover, we conducted sensitivity analysis on freight subsidies, train travel speeds, soft time windows, and carbon tax rates. The results showed that for medium- and long-distance cold chain transportation, the railway market share increased from 17.55% to 18.75% with an increase in the railway freight rate subsidy share from 0% to 30%. More cold chain goods were transported by rail when the soft time window or the carbon tax rate increased. Moreover, the railway market share increased from 17.55% to 43.75% with an increase in the train travel speed from 60 km/h to 120 km/h. Thus, compared with freight subsidy, increasing the train travel speed is a better approach to improve the competitiveness of railway cold chain logistics.

We extend the theory of input–output conformance (IOCO) testing to accommodate behavioral models of software product lines (SPLs). We present the notions of residual and spinal testing. These notions allow for structuring the test process for SPLs by taking variability into account and extracting separate test suites for common and specific features of an SPL. The introduced notions of residual and spinal test suites allow for focusing on the newly introduced behavior and avoiding unnecessary re-test of the old one. Residual test suites are very conservative in that they require retesting the old behavior that can reach to new behavior. However, spinal test suites more aggressively prune the old tests and only focus on those test sequences that are necessary in reaching the new behavior. We show that residual testing is complete but does not usually lead to much reduction in the test-suite. In contrast, spinal testing is not necessarily complete but does reduce the test-suite. We give sufficient conditions on the implementation to guarantee completeness of spinal testing. Finally, we specify and analyze an example regarding the Ceiling Speed Monitoring Function from the European Train Control System.

The availability of effective test suites is critical for the development and maintenance of reliable software systems. To increase test effectiveness, software developers tend to employ larger and larger test suites. The recent availability of software tools for automatic test generation makes building large test suites affordable, therefore contributing to accelerating this trend. However, large test suites, though more effective, are resources and time consuming and therefore cannot be executed frequently. Reducing them without decreasing code coverage is a needed compromise between efficiency and effectiveness of the test, hence enabling a more regular check of the software under development. We propose a novel approach, namely REDUNET, to reduce a test suite while keeping the same code coverage. We integrate this approach in a complete framework for the automatic generation of efficient and effective test suites, which includes test suite generation, code coverage analysis, and test suite reduction. Our approach formulates the test suite reduction as a set cover problem and applies integer linear programming and a network-based optimisation, which takes advantage of the properties of the control flow graph. We find the optimal set of test cases that keeps the same code coverage in fractions of seconds on real software projects and test suites generated automatically by Randoop. The results on ten real software systems show that the proposed approach finds the optimal minimisation and achieves up to 90% reduction and more than 50% reduction on all systems under analysis. On the largest project our reduction algorithm performs more than three times faster than both integer linear programming alone and the state-of-the-art heuristic Harrold Gupta Soffa.

The term grammar-based software describes software whose input can be specified by a context-free grammar. This grammar may occur explicitly in the software, in the form of an input specification to a parser generator, or implicitly, in the form of a hand-written parser. Grammar-based software includes not only programming language compilers, but also tools for program analysis, reverse engineering, software metrics and documentation generation. Hence, ensuring their completeness and correctness is a vital prerequisite for their use. In this paper we propose a strategy for the construction of test suites for grammar based software, and illustrate this strategy using the ISO C + + grammar. We use the concept of grammar-rule coverage as a pivot for the reduction of an implementation-based test suite, and demonstrate a significant decrease in the size of this suite. The effectiveness of this reduced test suite is compared to the original test suite with respect to code coverage and more importantly, fault detection. This work greatly expands upon previous work in this area and utilises large scale mutation testing to compare the effectiveness of grammar-rule coverage to that of statement coverage as a reduction criterion for test suites of grammar-based software. This work finds that when grammar rule coverage is used as the sole criterion for reducing test suites of grammar based software, the fault detection capability of that reduced test suite is greatly diminished when compared to other coverage criteria such as statement coverage.

Software product line (SPL) testing is a challenging task, due to the huge number of variants sharing common functionalities to be taken into account for efficient testing. By adopting the concept of regression testing, incremental SPL testing strategies cope with this challenge by exploiting the reuse potential of test artifacts between subsequent variants under test. In previous work, we proposed delta-oriented test case prioritization for incremental SPL integration testing, where differences between architecture test model variants allow for reasoning about the order of reusable test cases to be executed. However, the prioritization left two issues open, namely (1) changes to component behavior are ignored, which may also influence component interactions and, (2) the weighting and ordering of similar test cases result in an unintended clustering of test cases. In this paper, we extend the test case prioritization technique by (1) incorporating changes to component behavior allowing for a more fine-grained analysis and (2) defining a dissimilarity measure to avoid clustered test case orders. We prototyped our test case prioritization technique and evaluated its applicability and effectiveness by means of a case study from the automotive domain showing positive results.