This paper proposes a novel approach to generating an ontological perspective for software system. This ontology is a combination of two other ontologies: domain ontology and class diagram ontology. Domain ontology is domain vocabulary which is built by domain experts, while class diagram ontology is automatically populated from source code to represent the knowledge in the code. Combining these two ontologies will generate a third ontology which is used in program comprehension. Firstly, the motivation and related work are introduced. Secondly, method of class diagram to ontology transformation and algorithm of ontology combination are presented. Thirdly, program comprehension from an ontological point of view is discussed. Fourthly, a case study is demonstrated by understanding a point of sale terminal (POST) system. Finally, the conclusion is drawn and further research directions are speculated.
{"title":"Developing Application Specific Ontology for Program Comprehension by Combining Domain Ontology with Code Ontology","authors":"Hong Zhou, Feng Chen, Hongji Yang","doi":"10.1109/QSIC.2008.31","DOIUrl":"https://doi.org/10.1109/QSIC.2008.31","url":null,"abstract":"This paper proposes a novel approach to generating an ontological perspective for software system. This ontology is a combination of two other ontologies: domain ontology and class diagram ontology. Domain ontology is domain vocabulary which is built by domain experts, while class diagram ontology is automatically populated from source code to represent the knowledge in the code. Combining these two ontologies will generate a third ontology which is used in program comprehension. Firstly, the motivation and related work are introduced. Secondly, method of class diagram to ontology transformation and algorithm of ontology combination are presented. Thirdly, program comprehension from an ontological point of view is discussed. Fourthly, a case study is demonstrated by understanding a point of sale terminal (POST) system. Finally, the conclusion is drawn and further research directions are speculated.","PeriodicalId":6446,"journal":{"name":"2008 The Eighth International Conference on Quality Software","volume":"43 1","pages":"225-234"},"PeriodicalIF":0.0,"publicationDate":"2008-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84844161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the life time of software or even within the development stage, it is inevitable that the software needs to be modified. Test cases used before for the previous version could be reused in order to reduce the cost of regression testing. This paper proposes a regression testing methodology, called Partitioning of Domain Testing (PDT), to analyze the input domains of the previous version and the modified version so that test cases can be maximally reused. As a result, software test engineers can spend less effort in generating test cases to test the modified software. This methodology is different from traditional domain-based testing strategies in the sense that the partitioning is achieved by two supplementary perspectives, namely the specifications of the program and its testing criterion. First, the input domain is partitioned according to the specification and testing criterion of the program under test. Then, further refining methods are introduced to obtain 100% reusable test cases. We also illustrate the idea of PDT using a case study with 3 different testing criteria.
{"title":"On Partitioning the Domain for Test Case Reusability (Short Paper)","authors":"Yunwei Dong, M. Lau, Siemann Lin","doi":"10.1109/QSIC.2008.51","DOIUrl":"https://doi.org/10.1109/QSIC.2008.51","url":null,"abstract":"During the life time of software or even within the development stage, it is inevitable that the software needs to be modified. Test cases used before for the previous version could be reused in order to reduce the cost of regression testing. This paper proposes a regression testing methodology, called Partitioning of Domain Testing (PDT), to analyze the input domains of the previous version and the modified version so that test cases can be maximally reused. As a result, software test engineers can spend less effort in generating test cases to test the modified software. This methodology is different from traditional domain-based testing strategies in the sense that the partitioning is achieved by two supplementary perspectives, namely the specifications of the program and its testing criterion. First, the input domain is partitioned according to the specification and testing criterion of the program under test. Then, further refining methods are introduced to obtain 100% reusable test cases. We also illustrate the idea of PDT using a case study with 3 different testing criteria.","PeriodicalId":6446,"journal":{"name":"2008 The Eighth International Conference on Quality Software","volume":"55 1","pages":"264-269"},"PeriodicalIF":0.0,"publicationDate":"2008-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85642022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Boxology is the essence of software architecture description. In comparison with text-based languages,well-defined visual notations model software architecture at a high level of abstraction. They are easy to understand and easy to use due to its simplicity, but less expressive as many architectural properties can not be adequately represented. A key question to be answered in the design of a visual notation for the description of software architectures is what properties should be visually represented. This paper applies the theory of design space in the development of a visual notation called ExSAVN for software architectural modeling. It is based on the design space of software architectural elements to determine the properties of software architectures that are visually represented. It achieves balance between simplicity and expressiveness and supports incremental and iterative architectural design through a number of high level language facilities, which include the representation of undecided properties, hierarchical abstraction and type definition facilities. The paper also illustrated the style of ExSAVN by some examples of real systems.
{"title":"Visual Software Architecture Description Based on Design Space","authors":"Qian Zhang","doi":"10.1109/QSIC.2008.59","DOIUrl":"https://doi.org/10.1109/QSIC.2008.59","url":null,"abstract":"Boxology is the essence of software architecture description. In comparison with text-based languages,well-defined visual notations model software architecture at a high level of abstraction. They are easy to understand and easy to use due to its simplicity, but less expressive as many architectural properties can not be adequately represented. A key question to be answered in the design of a visual notation for the description of software architectures is what properties should be visually represented. This paper applies the theory of design space in the development of a visual notation called ExSAVN for software architectural modeling. It is based on the design space of software architectural elements to determine the properties of software architectures that are visually represented. It achieves balance between simplicity and expressiveness and supports incremental and iterative architectural design through a number of high level language facilities, which include the representation of undecided properties, hierarchical abstraction and type definition facilities. The paper also illustrated the style of ExSAVN by some examples of real systems.","PeriodicalId":6446,"journal":{"name":"2008 The Eighth International Conference on Quality Software","volume":"2 1","pages":"366-375"},"PeriodicalIF":0.0,"publicationDate":"2008-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87851286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. Random testing is a basic testing technique. Motivated by the observation that neighboring inputs normally exhibit similar failure behavior, the approach of adaptive random testing has recently been proposed to enhance the fault detection capability of random testing. The intuition of adaptive random testing is to evenly spread the randomly generated test cases. Experimental results have shown that adaptive random testing can use as fewer as 50% of test cases required by random testing with replacement to detect the first failure. These results have very significant impact in software testing, because random testing is a basic and popular technique in software testing. In view of such a significant improvement of adaptive random testing over random testing, it is very natural to consider to replace random testing by adaptive random testing. Hence, many works involving random testing may be worthwhile to be reinvestigated using adaptive random testing instead. Obviously, there are different approaches of evenly spreading random test cases. In this tutorial, we are going to present several approaches, and discuss their advantages and disadvantages. Furthermore, the favorable and unfavorable conditions for adaptive random testing would also be discussed. Most existing research on adaptive random testing involves only numeric programs. The recent success of applying adaptive random testing for non-numeric programs would be discussed.
{"title":"Adaptive Random Testing","authors":"Tsong Yueh Chen, H. Leung, I. K. Mak","doi":"10.1109/QSIC.2008.22","DOIUrl":"https://doi.org/10.1109/QSIC.2008.22","url":null,"abstract":"Summary form only given. Random testing is a basic testing technique. Motivated by the observation that neighboring inputs normally exhibit similar failure behavior, the approach of adaptive random testing has recently been proposed to enhance the fault detection capability of random testing. The intuition of adaptive random testing is to evenly spread the randomly generated test cases. Experimental results have shown that adaptive random testing can use as fewer as 50% of test cases required by random testing with replacement to detect the first failure. These results have very significant impact in software testing, because random testing is a basic and popular technique in software testing. In view of such a significant improvement of adaptive random testing over random testing, it is very natural to consider to replace random testing by adaptive random testing. Hence, many works involving random testing may be worthwhile to be reinvestigated using adaptive random testing instead. Obviously, there are different approaches of evenly spreading random test cases. In this tutorial, we are going to present several approaches, and discuss their advantages and disadvantages. Furthermore, the favorable and unfavorable conditions for adaptive random testing would also be discussed. Most existing research on adaptive random testing involves only numeric programs. The recent success of applying adaptive random testing for non-numeric programs would be discussed.","PeriodicalId":6446,"journal":{"name":"2008 The Eighth International Conference on Quality Software","volume":"93 1","pages":"443-443"},"PeriodicalIF":0.0,"publicationDate":"2004-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73851891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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