Structure-Oriented RTL Automatic Test Generation

The paper presents structure-oriented Register Transfer Level (RTL) test generation algorithm, which hierarchically tests large-scale circuits. It generates tests for low-level circuit with gate-level test generation technology, and generates tests for high-level circuit with combining module test sets. It also presents a new fault-simulation algorithm at RT level circuit to adapt test generation hierarchically.

Test Generation and Design-for-Testability Based on Acyclic Structure with Hold Registers

We present a method of test generation for acyclic sequential circuits with hold registers. A complete (100% fault efficiency) test sequence for an acyclic sequential circuit can be obtained by applying a combinational test generator to all the maximal time-expansion models (TEMs) of the circuit. We propose a class of acyclic sequential circuits for which the number of maximal TEMs is one, i.e, the maximum TEM exists. For a circuit in the class, test generation can be performed by using only the maximum TEM. The proposed class of sequential circuits with the maximum TEM properly includes several known classes of acyclic sequential circuits such as balanced structures and acyclic sequential circuits without hold registers for which test generation can be also performed by using a combinational test generator. Therefore, in general, the hardware overhead for partial scan based on the proposed structure is smaller than that based on balanced or acyclic sequential structure without hold registers.

Forecasting and Evaluating the Efficiency of Test Generation Algorithms by Genetic Algorithm

To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.

Target-Fault-Oriented Test Generation of Sequential CircuitsUsing Genetic Algorithm

This paper deals with the target-fault-oriented test generation of sequential circuits using genetic algorithms. We adopted the concept of multiple phases and proposed four sub-procedures which consist of activation, propagation and justification phases. The paper focuses on the design of genetic operators and construction of fitness functions which are based on the structure information of circuits. Using ISCAS89 benchmarks, the experiment results of GA were given.

STG-BASED VERIFICATION AND TEST GENERATION

This paper presents the techniques of verification and Test Generation(TG) for sequential machines (Finite State Machines, FSMs) based on state traversing of State Transition Graph(STG). The problems of traversing, redundancy and transition fault model are identified. In order to achieve high fault coverage collapsing testing is proposed. Further, the heuristic knowledge for speeding up verification and TG are described.

An Empirical Study on Automated Test Generation Tools for Java:Effectiveness and Challenges

Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test cases.However,existing automated tools are not mature enough to be widely used by software testing groups.This paper conducts an empirical study on the state-of-the-art automated tools for Java,i.e.,EvoSuite,Randoop,JDoop,JTeXpert,T3,and Tardis.We design a test workflow to facilitate the process,which can automatically run tools for test generation,collect data,and evaluate various metrics.Furthermore,we conduct empirical analysis on these six tools and their related techniques from different aspects,i.e.,code coverage,mutation score,test suite size,readability,and real fault detection ability.We discuss about the benefits and drawbacks of hybrid techniques based on experimental results.Besides,we introduce our experience in setting up and executing these tools,and summarize their usability and user-friendliness.Finally,we give some insights into automated tools in terms of test suite readability improvement,meaningful assertion generation,test suite reduction for random testing tools,and symbolic execution integration.

The structure-based multi-fault test generation algorithm for combinational circuit

In this paper the structure-based test generation algorithm has been studied for the problem that test patterns are obtained by determined finite faults set in the past. This Algorithm can find out all test patterns one tithe, so faults detection is very convenient. By simulation, the smallest test patterns set can be obtained and faults coverage rate is 100%.

Research of the test generation algorithm based on search state dominance for combinational circuit

On the basis of EST (Equivalent STate hashing) algorithm, this paper researches a kind of test generation algorithm based on search state dominance for combinational circuit. According to the dominance relation of the E-frontier (evaluation frontier), we can prove that this algorithm can terminate unnecessary searching step of test pattern earlier than the EST algorithm through some examples, so this algorithm can reduce the time of test generation. The test patterns calculated can detect faults given through simulation.

A new approach to test generation for combinational circuits

Aimed at the generation of high-quality test set in the shortest possible time, the test generation for combinational circuits （CC） based on the chaotic particle swarm optimization （CPSO） algorithm is presented according to the analysis of existent problems of CC test generation, and an appropriate CPSO algorithm model has been constructed. With the help of fault simulator, the test set of ISCAS＇ 85 benchmark CC is generated using the CPSO, and some techniques are introduced such as half-random generation, and simulation of undetected fauhs.with original test vector, and inverse test vector. Experimental results show that this algorithm can generate the same fault coverage and small-size test set in short time compared with other known similar methods, which proves that the proposed method is applicable and effective.

Study on MCM Interconnect Test Generation Based on Ant Algorithm with Mutation Operator

A novel multi-chip module(MCM) interconnect test generation scheme based on ant algorithm(AA) with mutation operator was presented.By combing the characteristics of MCM interconnect test generation,the pheromone updating rule and state transition rule of AA is designed.Using mutation operator,this scheme overcomes ordinary AA’s defects of slow convergence speed,easy to get stagnate,and low ability of full search.The international standard MCM benchmark circuit provided by the MCNC group was used to verify the approach.The results of simulation experiments,which compare to the results of standard ant algorithm,genetic algorithm(GA) and other deterministic interconnecting algorithms,show that the proposed scheme can achieve high fault coverage,compact test set and short CPU time,that it is a newer optimized method deserving research.

Test Case Generation Evaluator for the Implementation of Test Case Generation Algorithms Based on Learning to Rank

In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.

Two stages power generation test of the hot dry rock exploration and production demonstration project in the Gonghe Basin,northeastern Qinghai-Tibet plateau,China

The Hot Dry Rock(HDR)is considered as a clean and renewable energy,poised to significantly contribute to the global energy decarbonization agenda.Many HDR projects worldwide have accumulated valuable experience in efficient drilling and completion,reservoir construction,and fracture simulation.In 2019,China Geological Survey(CGS)initiated a demonstration project of HDR exploration and production in the Gonghe Basin,aiming to overcome the setbacks faced by HDR projects.Over the ensuing four years,the Gonghe HDR project achieved the first power generation in 2021,followed by the second power generation test in 2022.After establishing the primary well group in the initial phase,two directional wells and one branch well were drilled.Noteworthy progress was made in successfully constructing the targeted reservoir,realizing inter-well connectivity,power generation and grid connection,implementing of the real-time micro-seismic monitoring.A closed-loop technical validation of the HDR exploration and production was completed.However,many technical challenges remain in the process of HDR industrialization,such as reservoir fracture network characterization,efficient drilling and completion,multiple fracturing treatment,continuous injection and production,as well as mitigation of induced seismicity and numerical simulation technology.

Implications of genetic testing and informed consent before and after genetic testing in individuals with cancer

Recent advancements in next generation sequencing have allowed for genetic information become more readily available in the clinical setting for those affected by cancer and by treating clinicians.Given the lack of access to geneticists,medical oncologists and other treating physicians have begun ordering and interpreting genetic tests for individuals with cancer through the process of"mainstreaming".While this process has allowed for quicker access to genetic tests,the process of"mainstreaming"has also brought several challenges including the dissemination of variants of unknown significance results,ordering of appropriate tests,and accurate interpretation of genetic results with appropriate followup testing and interventions.In this editorial,we seek to explore the process of informed consent of individuals before obtaining genetic testing and offer potential solutions to optimize the informed consent process including categorization of results as well as a layered consent model.

Forecasting the Efficiency of Test Generation Algorithms for Combinational Circuits

In this era of VLSI circuits, testability is truly a very crucial issue.To generate a test set for a given circuit, choice of an algorithm from a number ofexisting test generation algorithms to apply is bound to vary from circuit to circuit.In this paper, the Genetic Algorithm is used in order to construct an accurate modelfor some existing test generation algorithms that are being used everywhere in theworld. Some objective quantitative measures are used as an effective tool in makingsuch choice. Such measures are so important to the analysis of algorithms that theybecome one of the subjects of this work.

DLJ:A Dynamic Line-Justification Algorithm for Test Generation

Line justification is a basic factor in affecting the efficiency of algorithms for test generation.The existence of reconvergent fanouts in the circuit under test resalts in backtracks in the process of line justification.In order to reduce the number of backtracks and shorten the processing time between backtracks,we present a new algorithm called DLJ(dynamic line justification)in which two techniques are employed.1.A cost function called“FOCOST”is proposed as heuristic information to represent the cost of justifying a certain line.When the relations among the lines being justified are“and”,the line having the highest FOCOST should be chosen.When the relations are“or”,the line having the lowest FOCOST should be chosen.The computing of the FOCOST of lines is very simple.2. Disjoint justification cubes dynamically generated to perform backtracks make the backtrack number of the algorithm minimal.When the backtrace with cube C_1 does not yield a solution,the next cube to be chosen is C′_2=C_2-{C_1,C_2}.Experimental results demonstrate that the combination of the two techniques effectively reduces the backtracks and accelerates the test generation.

A Complete Critical Path Algorithm for Test Generation of Combinational Circuits

It is known that critical path test generation method is not a complete algorithm for combinational circuits with reconvergent-fanout.In order to make it a complete algorithm,we put forward a reconvergent-fanout- oriented technique,the principal critical path algorithm,propagating the critical value back to primary inputs along a single path,the principal critical path,and allowing multiple path sensitization if needed.Relationship among test patterns is also discussed to accelerate test generation.

SMINER:Detecting Unrestricted and Misimplemented Behaviors of Software Systems Based on Unit Test Cases

Despite the advances in automated vulnerability detection approaches,security vulnerabilities caused by design flaws in software systems are continuously appearing in real-world systems.Such security design flaws can bring unrestricted and misimplemented behaviors of a system and can lead to fatal vulnerabilities such as remote code execution or sensitive data leakage.Therefore,it is an essential task to discover unrestricted and misimplemented behaviors of a system.However,it is a daunting task for security experts to discover such vulnerabilities in advance because it is timeconsuming and error-prone to analyze the whole code in detail.Also,most of the existing vulnerability detection approaches still focus on detecting memory corruption bugs because these bugs are the dominant root cause of software vulnerabilities.This paper proposes SMINER,a novel approach that discovers vulnerabilities caused by unrestricted and misimplemented behaviors.SMINER first collects unit test cases for the target system from the official repository.Next,preprocess the collected code fragments.SMINER uses pre-processed data to show the security policies that can occur on the target system and creates a test case for security policy testing.To demonstrate the effectiveness of SMINER,this paper evaluates SMINER against Robot Operating System(ROS),a real-world system used for intelligent robots in Amazon and controlling satellites in National Aeronautics and Space Administration(NASA).From the evaluation,we discovered two real-world vulnerabilities in ROS.

Generating of Test Data by Harmony Search Against Genetic Algorithms

Many search-based algorithms have been successfully applied in sev-eral software engineering activities.Genetic algorithms(GAs)are the most used in the scientific domains by scholars to solve software testing problems.They imi-tate the theory of natural selection and evolution.The harmony search algorithm(HSA)is one of the most recent search algorithms in the last years.It imitates the behavior of a musician tofind the best harmony.Scholars have estimated the simi-larities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains.The test data generation process represents a critical task in software validation.Unfortunately,there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process.This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed offinding the required test data.The current research performs an empirical comparison of the HSA and the GAs,and then the significance of the results is estimated using the t-Test.The study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to(1)the time performance,(2)the significance of the generated test data,and(3)the adequacy of the generated test data to satisfy a given testing criterion.The results showed that the harmony search algorithm is significantly faster than the genetic algo-rithms because the t-Test showed that the p-value of the time values is 0.026<α(αis the significance level=0.05 at 95%confidence level).In contrast,there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of thefitness values is 0.25>α.

Current scenario of the genetic testing for rare neurological disorders exploiting next generation sequencing

Next generation sequencing is currently a cornerstone of genetic testing in routine diagnostics,allowing for the detection of sequence variants with so far unprecedented large scale,mainly in genetically heterogenous diseases,such as neurological disorders.It is a fast-moving field,where new wet enrichment protocols and bioinformatics tools are constantly being developed to overcome initial limitations.Despite the as yet undiscussed advantages,however,there are still some challenges in data analysis and the interpretation of variants.In this review,we address the current state of next generation sequencing diagnostic testing for inherited human disorders,particularly giving an overview of the available high-throughput sequencing approaches;including targeted,whole-exome and whole-genome sequencing;and discussing the main critical aspects of the bioinformatic process,from raw data analysis to molecular diagnosis.

Automated Test Case Generation from Requirements: A Systematic Literature Review

Software testing is an important and cost intensive activity in software development.The major contribution in cost is due to test case generations.Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure.Requirement-based testing includes functional and nonfunctional requirements.The objective of this study is to explore the approaches that generate test cases from requirements.A systematic literature review based on two research questions and extensive quality assessment criteria includes studies.The study identies 30 primary studies from 410 studies spanned from 2000 to 2018.The review’s nding shows that 53%of journal papers,42%of conference papers,and 5%of book chapters’address requirementsbased testing.Most of the studies use UML,activity,and use case diagrams for test case generation from requirements.One of the signicant lessons learned is that most software testing errors are traced back to errors in natural language requirements.A substantial amount of work focuses on UML diagrams for test case generations,which cannot capture all the system’s developed attributes.Furthermore,there is a lack of UML-based models that can generate test cases from natural language requirements by rening them in context.Coverage criteria indicate how efciently the testing has been performed 12.37%of studies use requirements coverage,20%of studies cover path coverage,and 17%study basic coverage.