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Regression Testing Prioritization Based on Fuzzy Inference <strong>Systems</strong> Pedro Santos PedroNeto Santos - UFPI Neto- Teresina - UFPI -- Teresina PI - Brazil - PI - Brazil pasn@ufpi.edu.br pasn@ufpi.edu.br Thiago Soares - UFPI --Teresina --PI PI- -Brazil thiagoacs2@gmail.com thiagoacs2@gmail.com Ricardo Britto Britto - UFPI - UFPI - Teresina - Teresina - PI - PI - Brazil - Brazil pasn@ufpi.edu.br Werney Werney Ayala Ayala - UFPI - UFPI - Teresina - Teresina - PI - - PI Brazil - Brazil werney@gmail.com werney@gmail.com Jonathas Cruz UFPI Teresina - PI - Brazil Jonathas Cruz - UFPI - Teresina - PI - Brazil jonathas jivago@hotmail.com jonathas jivago@hotmail.com Ricardo Rabelo - USP - São Paulo - SP - Brazil Ricardo Rabelo - USP - S˜ao Paulo - SP - Brazil ricardor@sc.usp.br ricardor@sc.usp.br Abstract—The software testing is a fundamental activity related to product quality. However, it is not performed in suitable way by many organizations. It is necessary to execute testing in a systematic and planned way. This work presents a fuzzy inference system for test case prioritization, based on the use of inputs related to volatility, complexity and relevance of requirements. The developed inference system allows the specification of a prioritization strategy by the tester based on linguistic rules in asimple and easy way. I. INTRODUCTION Testing is the final verification of a software product [1]. Despite of the relevance of this phase on a project, testing is not systematic fulfilled for the enterprises. That situation happens mainly because of the costs and the short schedules related to the software projects. The software engineering community must find ways to facilitate the inclusion of systematic testing into software processes. In some enterprise environments, which use software testing to assure the software quality, might be necessary prioritize existing test cases because often there is not enough time or resources to execute all planned test cases. In this case, it is desirable to prioritize the test cases, in order to maintain the most important ones in the first positions, assuring their execution. To do that, it is necessary create a execution order that optimizes the most important features for the testing in a certain time, such as code covering. Since creating the test execution order is a np-hard problem [2], it is not possible to solve that problem in an efficient way through standard techniques [3]. Thus, many researchers have applied computational intelligence techniques to solve the test case prioritization problem ([4], [5], [6]). This work presents an approach based on Mamdani fuzzy inference system [7] that uses requirements to solve the test case prioritization problem ([8], [5], [9]). The approach allows that a test engineer can insert his knowledge about the prioritization process into the fuzzy inference system. Therefore, the fuzzy inference system mimics the behavior of the expert. The main contribution of this work is the use of a fuzzy inference system to qualify each existing test case of a software project. This feature allows the prioritization through asimple sort algorithm, driven by the value inferred for each test case, based on common input data as relevance of the requirement for the project, its complexity and volatility. The complexity and volatility can be easily obtained from common software metric tools, like Sonar 1 . This work is organized as following: in Section 2 is presented a software testing primer; in Section 3 a fuzzy inference system is described; in Section 4 the proposed approach is detailed; in Section 5 an empirical evaluation is presented; in Section 6 some related works are discussed; finally, in Section 7 the conclusions and future works are presented. II. SOFTWARE TESTING PRIMER Software Testing is one of the main activities of quality assurance in software development. Software testing consists of the dynamic verification of the behavior of a program on a finite set of test cases, suitably selected from the usually infinite executions domain, against the expected behavior. There are different classifications related to software testing. Tests can be classified by the target: one module (unit), several modules grouped (integration) and all the modules together (system). Testing can be aimed at verifying different properties. This represents the test classification by objectives. Test cases can be designed to check that the functional specifications are correctly implemented (functional testing). Other important objectives for testing include (but are not limited to) reliability measurement, usability evaluation, performance, stress and acceptance, for which different approaches would be taken. Note that the test objective varies with the test target; in general, different purposes being addressed at a different level of testing. 1 http://www.sonarsource.org/ 273
According to IEEE [10], regression testing is the selective retesting of a system or component to verify that modifications have not caused unintended effects. In practice, the idea is to show that software did not regress, that is the software which previously passed the tests still does. The repetition of tests is intended to show that the softwares behavior is unchanged, except insofar as required. Obviously a trade-off must be made between the assurance given by regression testing every time a change is made and the resources required to do that. A important limiting factor applied to regression testing is related to time-constraints. Sometimes, the total execution of testing can take many hours. This makes unfeasible the test execution constantly. Due to this factor it is required to prioritize the test cases, in order to create the best sequence that meets the time constraints. It is relevant to emphasize that the selection must be found quickly and the process to find this must not require the tests execution, since the objective is right to reduce the test execution. Unfortunately, several approaches developed so far, mainly related to the use of search based techniques applied to this problem, are only theoretical proposals, that can not be executed in a real software development environment. It is mandatory the use of information that can be easily obtained by automatic tools. Therefore, it is mandatory the use of algorithms that do not require the tests execution to find the best order. The sequence must be inferred from test properties. III. FUZZY INFERENCE SYSTEMS Fuzzy inference systems are based on linguistic production rules like ”if ... then”. The fuzzy sets theory [11] and fuzzy logic [12] provide the math base required to deal with complex processes, based on inaccurate, uncertain and qualitative information. Fuzzy inference systems have their operation based on three steps: i) fuzzification, ii) inference procedures, and iii) defuzzification. The fuzzification is a mapping of the numerical inputs to fuzzy sets. Those sets are represented by linguistic terms, such as ”very”, ”little”, ”medium”, etc. The fuzzy inference procedure is responsible to infer output fuzzified value based on the input values. Defuzzification is applied to associate a numerical value to a fuzzy output, obtained from the fuzzy inference procedure. In this work, we applied a Mamdani fuzzy inference system model. That system model was applied because it computationally simulate the human ability to take rational decisions in an environment with inaccuracies, uncertainties and noise [7]. Production rules in a Mamdani inference model have fuzzy sets in their antecedents and consequents. Therefore, a rule base in a Mamdani model can be defined exclusively in a linguistic way, without the need of numerical data. A fuzzy inference systems can express and handle qualitative information. This allows the mapping of the experience of domain specialists, facilitating the decision making process. Thus, using a Mamdani fuzzy inference system, to solve the test case prioritization problem, allows to get an action strategy / control that can be monitored and interpreted from the linguistic point of view. Thus, the action / control strategy of the Mamdani fuzzy inference system can be considered as reasoned and consistent as the strategy of domain experts. IV. PROPOSED APPROACH Our approach evaluates the requirements to infer the test case criticality for regression testing purpose. However, test cases from real systems can eventually cover artifacts that may be related to more than one requirement. In the current stage of our work we do not take into account this behavior. Each requirement is evaluated based on 3 variables that represent the volatility, complexity and relevance. By using these variables a value that represents the criticality of a test case is generated. We projected a Mamdani fuzzy inference system to infer the test case criticality, as shown in Figure 1. The crisp input variables of the fuzzy inference system are Volatility, Complexity and Relevance. The meaning of each variable is described as following: • Volatility (V): value representing the amount of versions of the artifacts related to a requirement. The amount of versions can be easily obtained from a control version systems like GIT 2 and SVN 3 . • Complexity (C): value representing the ciclomatic complexity of the artifacts related to a requirement. This can be easily obtained from acommon software metrics tools, like Sonar. • Relevance (R): value representing the relevance of the requirement for the customer. The values related to volatility and complexity are calculated from the data obtained for each artifact associated to a requirement. Thus, the volatility for a specific requirement is obtained from the volatility of the whole classes linked to the requirement implementation. To do this, we use the traceability matrix [1] to obtain the forward traceability information. The 3 input variables can receive real values between 0 and 5. The output variable of the fuzzy inference system is Criticality of the Test Case (CTC). The bigger the CTC, the bigger the test case execution priority. Thus, the test case with the biggest inferred CTC is the first to be executed. The CTC output variable can receive real values between 0 and 10, representing the real values of that variable. To map the fuzzy sets, we used a uniform distribution of the sets. This is the normal process to do this task. After done the uniform distribution of the fuzzy sets, it is possible to improve the mapping empirically or using computational intelligence techniques such genetic algorithms [13]. We used triangular and trapezoidal membership functions to empirically map fuzzy sets to the input variables. To each input variable 3 fuzzy sets were mapped: Low (L), Medium (M) and High (H). We also used triangular membership functions to empirically map fuzzy sets to the output variable. We mapped 2 http://git-scm.com/ 3 http://subversion.tigris.org/ 274
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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Cloud Application Resource Mapping
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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A Tiny Specification Metalanguage W
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
Page 705 and 706:
means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
Page 715 and 716:
data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
Page 727 and 728:
software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
Page 747 and 748:
A Tool for Visualization of a Knowl
Page 749 and 750:
other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
Page 757 and 758:
ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
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Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
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two stages. The first stage is focu
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754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
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information. If more than one adver
Page 801 and 802:
Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
Page 807 and 808:
Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816:
Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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