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Bidirectional Symbolic Analysis for Effective branch Testing

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2016年第5期42卷 403-426页

作者： Baluda, Mauro Denaro, Giovanni Pezze, Mauro Fraunhofer SIT Secure Software Engn Grp Darmstadt Germany Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Univ Svizzera Italiana Fac Informat Lugano Switzerland

Structural coverage metrics, and in particular branch coverage, are popular approaches to measure the thoroughness of test suites. Unfortunately, the presence of elements that are not executable in the program under test and the difficulty of generating test cases for rare conditions impact on the effectiveness of the coverage obtained with current approaches. In this paper, we propose a new approach that combines symbolic execution and symbolic reachability analysis to improve the effectiveness of branch testing. Our approach embraces the ideal definition of branch coverage as the percentage of executable branches traversed with the test suite, and proposes a new bidirectional symbolic analysis for both testing rare execution conditions and eliminating infeasible branches from the set of test objectives. The approach is centered on a model of the analyzed execution space. The model identifies the frontier between symbolic execution and symbolic reachability analysis, to guide the alternation and the progress of bidirectional analysis towards the coverage targets. The experimental results presented in the paper indicate that the proposed approach can both find test inputs that exercise rare execution conditions that are not identified with state-of-the-art approaches and eliminate many infeasible branches from the coverage measurement. It can thus produce a modified branch coverage metric that indicates the amount of feasible branches covered during testing, and helps team leaders and developers in estimating the amount of not-yet-covered feasible branches. The approach proposed in this paper suffers less than the other approaches from particular cases that may trap the analysis in unbounded loops.

关键词： Structural testing branch coverage program analysis symbolic execution symbolic reachability analysis

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Automated test data generation for branch testing using incremental genetic algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2016年第9期41卷 959-976页

作者： Manikumar, T. Kumar, A. John Sanjeev Maruthamuthu, R. RVS Coll Engn Dept Comp Applicat Dindigul 624005 Tamil Nadu India Thiagarajar Coll Engn Dept Comp Applicat Madurai 625015 Tamil Nadu India

Cost of software testing can be reduced by automated test data generation to find a minimal set of data that has maximum coverage. Search-based software testing (SBST) is one of the techniques recently used for automated testing task. SBST makes use of control flow graph (CFG) and meta-heuristic search algorithms to accomplish the process. This paper focuses on test data generation for branch coverage. A major drawback in using meta-heuristic techniques is that the CFG paths have to be traversed from the starting node to end node for each automated test data. This kind of traversal could be improved by branch ordering, together with elitism. But still the population size and the number of iterations are maintained as the same to keep all the branches alive. In this paper, we present an incremental genetic algorithm (IGA) for branch coverage testing. Initially, a classical genetic algorithm (GA) is used to construct the population with the best parents for each branch node, and the IGA is started with these parents as the initial population. Hence, it is not necessary to maintain a huge population size and large number of iterations to cover all the branches. The performance is analyzed with five benchmark programs studied from the literature. The experimental results indicate that the proposed IGA search technique outperforms the other meta-heuristic search techniques in terms of memory usage and scalability.

关键词： Search-based software testing branch coverage test data generation genetic algorithm

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Weighted Particle Swarm Optimization Algorithm for Test Data Generation

Weighted Particle Swarm Optimization Algorithm for Test Data...

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International Conference on Computing, Analytics and Security Trends (CAST)

作者： Gopi, Pooja Ramalingam, Mohanasundari Maruthaperumal, Anand Kumar Panakkal, Sayooj Murugan, Jayashri Arumugam, Chamundeswari SSN Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9781509013388

Recently, Search Based Software Testing (SBST) research has gained much attention in producing the optimal solution for the optimization problem by automating the test data generation for the branch coverage criterion. Particle Swarm Optimization (PSO) has been emerged for obtaining optimal solution for the test data generation problem because of its easy implementation, fast convergence and few parameters. An attempt has been made in this paper to apply a weight based swarm intelligence algorithm called Weighted Particle Swarm Optimization (WPSO) to automate the test data generation for the branch coverage criterion. A fitness function including weight parameter is proposed to use in WPSO algorithm for evaluating the fitness using minimization approach. This fitness function aims to achieve high coverage and fast convergence. A benchmark program is used for the experimental analysis to compare the high coverage and fast convergence obtained from WPSO and PSO algorithms.

关键词： SBST Weighted Particle Swarm Optimization branch coverage Fitness function

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Efficient Data-Race Detection with Dynamic Symbolic Execution

Efficient Data-Race Detection with Dynamic Symbolic Executio...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Ibing, Andreas Tech Univ Munich Chair IT Secur Bollzmannstr 3 D-85748 Garching Germany

ISBN: (纸本)9788360810903

This paper presents data race detection using dynamic symbolic execution and hybrid lockset / happens-before analysis. Symbolic execution is used to explore the execution tree of multi-threaded software for FIFO scheduling on a single CPU core. Compared to exploring the joint scheduling and execution tree, the combinatorial explosion is drastically reduced. An SMT solver is used to control a debugger's machine interface for adaptive dynamic instrumentation to drive program execution into desired paths. Data races are detected in concrete execution with available static binary instrumentation using hybrid analysis. State interpolation using unsatisfiable cores is employed for path pruning, to avoid exploration of paths that do not contribute to increasing branch coverage. An implementation in Eclipse CDT is described and evaluated with data race test cases from the Juliet C/C++ test suite for program analyzers.

关键词： race detection symbolic execution interpolation branch coverage

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Search Based Test Data Generation: A Multi Objective Approach using MOPSO Evolutionary Algorithm 16

Search Based Test Data Generation: A Multi Objective Approac...

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9th Annual ACM India Conference (COMPUTE)

作者： Gopi, Pooja Ramalingam, Mohanasundari Arumugam, Chamundeswari SSN Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781450348089

Search based test data generation plays an important role in software testing. Several search based evolutionary algorithms are used to find the optimal test data. Among these algorithms, a meta-heuristic algorithm called Particle Swarm Optimization (PSO) algorithm is adopted for finding the optimal test data for the given Software Under Test (SUT) due to its simplicity and fast convergence. The success of PSO as a single objective optimizer in the literature has motivated to solve multi objective optimization problems. Hence, Multi Objective Particle Swarm Optimization (MOPSO) is adopted for solving more than one objective. This research work consider two objectives which attempts to maximize the branch coverage and reduce the test suite size. A benchmark program is used for the experimental analysis using MOPSO algorithm. The experimental analysis was performed using MOTestGen tool to extract the results. The extracted results portraits the convergence and coverage performance in producing the optimal test data as the population size increases.

关键词： branch coverage test suite fitness function multi objective MOPSO

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Adapting ant colony optimization to generate test data for software structural testing

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SWARM AND EVOLUTIONARY COMPUTATION 2015年 20卷 23-36页

作者： Mao, Chengying Xiao, Lichuan Yu, Xinxin Chen, Jinfu Jiangxi Univ Finance & Econ Sch Software & Commun Engn Nanchang 330013 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Jiangsu Univ Sch Comp Sci & Telecommun Engn Zhenjiang 212013 Jiangsu Peoples R China

In general, software testing has been viewed as an effective way to improve software quality and reliability. However, the quality of test data has a significant impact on the fault-revealing ability of software testing activity. Recently, search-based test data generation has been treated as an operational approach to settle this difficulty. In the paper, the basic ACO algorithm is reformed into discrete version so as to generate test data for structural testing. First, the technical roadmap of combining the adapted ACO algorithm and test process together is introduced. In order to improve algorithm's searching ability and generate more diverse test inputs, some strategies such as local transfer, global transfer and pheromone update are defined and applied. The coverage for program elements is a special optimization objective, so the customized fitness function is constructed in our approach through comprehensively considering the nesting level and predicate type of branch. To validate the effectiveness of our ACO-based test data generation method, eight well-known programs are utilized to perform the comparative analysis. The experimental results show that our approach outperforms the existing simulated annealing and genetic algorithm in the quality of test data and stability, and is comparable to particle swarm optimization-based method. In addition, the sensitivity analysis on algorithm parameters is also employed to recommend the reasonable parameter settings for practical applications. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Test data generation Meta-heuristic search Ant colony optimization branch coverage Fitness function Experimental evaluation

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branch Guided Functional Test Generation at the RTL 20

Branch Guided Functional Test Generation at the RTL

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20th IEEE European Test Symposium (ETS)

作者： Acharya, Vineeth V. Bagri, Sharad Hsiao, Michael S. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781479976034

In this paper, we propose a functional test generation method for Register Transfer Level circuits. A popular metric for measuring the effectiveness of an RTL test suite is branch coverage. The challenge in exercising a hard-to-reach branch is in the understanding of the semantics of the design. Without a good guidance, hard branches might require unnecessarily long test sequences or missed altogether. In our method, we extract such semantics from the circuit using a lightweight static analysis of the code in order to guide the search. Experimental results show that a high level of coverage can be obtained for several benchmark circuits, while reducing the test vector lengths by up to two orders of magnitude in some circuits.

关键词： Algorithms Test Generation RTL branch coverage

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Using Artificial Bee Colony for Code coverage based Test Suite Prioritization 2

Using Artificial Bee Colony for Code Coverage based Test Sui...

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2nd International Conference on Information Science and Security (ICISS 2015)

作者： Konsaard, Patipat Ramingwong, Lachana Chiang Mai Univ Dept Comp Engn Chiang Mai Thailand

ISBN: (纸本)9781467386111

The goal of test suite prioritization is maximizing fault detection and code coverage rate. Several nature inspired optimization algorithms such as Swarm Intelligence (SI) have been studied for the optimization of such problems. The studies revealed the benefits of Artificial Bee Colony (ABC) over other algorithms. ABC and its variations were implemented in software testing areas, test suite prioritization in particular. However, most SI based approaches focus on fault detection ability which is difficult to predict. In this paper, the standard ABC algorithm is used to prioritize test suites based on code coverage. The results reveal that ABC shows promising results and, hence, is a great candidate for prioritizing test suites. It also suggests that a modification to the standard ABC algorithm or combination of ABC and another SI algorithm should yield an even better result.

关键词： Test suite prioritization Test suite Regression testing Artificial Bee Colony ABC Code coverage branch coverage APCC Software Testing Software engineering

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branch Guided Metrics for Functional and Gate-level Testing

Branch Guided Metrics for Functional and Gate-level Testing

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作者： Acharya, Vineeth Vadiraj Virginia Tech | University

With the increasing complexity of modern day processors and system-on-a-chip (SOCs), designers invest a lot of time and resources into testing and validating these designs. To reduce the time-to-market and cost, the techniques used to validate these designs have to constantly improve. Since most of the design activity has moved to the register transfer level (RTL), test methodologies at the RTL have been gaining momentum. We present a novel functional test generation framework for functional test generation at RTL. A popular software-based metric for measuring the effectiveness of an RTL test suite is branch coverage. But exercising hard-to-reach branches is still a challenge and requires good understanding of the design semantics. The proposed framework uses static analysis to extract certain semantics of the circuit and uses several data structures to model these semantics. Using these data structures, we assist the branch-guided search to exercise these hard-to-reach branches. Since the correlation between high branch coverage and detecting defects and bugs is not clear, we present a new metric at the RTL which augments the RTL branch coverage with state values. Vectors which have higher scores on the new metric achieve higher branch and state coverages, and therefore can be applied at different levels of abstraction such as post-silicon validation. Experimental results show that use of the new metric in our test generation framework can achieve a high level of branch and fault coverage for several benchmark circuits, while reducing the length of the vector sequence. This work was supported in part by the NSF grant 1016675.

关键词： branch coverage Algorithms Fault coverage State-augmented branch Score Functional Test Generation Thesis

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Does Automated Unit Test Generation Really Help Software Testers? A Controlled Empirical Study

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2015年第4期24卷 23-23页

作者： Fraser, Gordon Staats, Matt McMinn, Phil Arcuri, Andrea Padberg, Frank Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Univ Luxembourg SnT Ctr Secur Reliabil & Trust L-2721 Luxembourg Luxembourg Simula Res Lab Certus Software V&V Ctr Lysaker Norway Karlsruhe Inst Technol D-76021 Karlsruhe Germany

Work on automated test generation has produced several tools capable of generating test data which achieves high structural coverage over a program. In the absence of a specification, developers are expected to manually construct or verify the test oracle for each test input. Nevertheless, it is assumed that these generated tests ease the task of testing for the developer, as testing is reduced to checking the results of tests. While this assumption has persisted for decades, there has been no conclusive evidence to date confirming it. However, the limited adoption in industry indicates this assumption may not be correct, and calls into question the practical value of test generation tools. To investigate this issue, we performed two controlled experiments comparing a total of 97 subjects split between writing tests manually and writing tests with the aid of an automated unit test generation tool, EVOSUITE. We found that, on one hand, tool support leads to clear improvements in commonly applied quality metrics such as code coverage (up to 300% increase). However, on the other hand, there was no measurable improvement in the number of bugs actually found by developers. Our results not only cast some doubt on how the research community evaluates test generation tools, but also point to improvements and future work necessary before automated test generation tools will be widely adopted by practitioners.

关键词： Algorithms Experimentation Reliability Theory Unit testing automated test generation branch coverage empirical software engineering

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