# Test effort: A pre-conceptual-schema-based representation

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3 Figure 3. The main symbols of pre-conceptual schema. Source: [11] Figure 5. The V-Model. Source: [13] for executing the test suite [2]. The test suite is defined in a controlled language. In Fig. 6 the estimation process is summarized. Figure 4. Overview of test point analysis procedure. Source: [12] 3.2. Test-case-enumeration-based estimation Test point analysis Primary test hours are obtained from three factors: number of test points, environmental factor, and productivity factor [12]. The number of test points is calculated by using the amount of function points (FP) for each individual function. Total number of the test hours is the sum of primary test hours plus secondary test hours. Total number of the test hours represents total time needed to complete a set of the testing activities. In the Fig. 4 an overview about the test point analysis is shown. The figure represents the flow of information needed for the test-point-based estimation. However, the way to obtain the factors is uncertain. Since the arrow syntax is intuitive, the graph representation appears incomplete because the concepts and their relationships can not be obtained Use-case-based estimation (test point analysis) The number of tests cases is obtained by mapping the uses case specifications into the test process [13]. Each scenario and its exception flows for each use cases are inputs for test cases. After that, the effort estimation is calculated. In the Fig. 5, the project lifecycle model used is illustrated, but the graphical representation describing the test point method for test effort estimation is not presented. First the test cases are enumerated, next different scenarios are evaluated for each test case and the effort is calculated Test-specification-based estimation Input for the approach is a test suite and the output is a number which represents the effort in man-hours required Figure 6. Estimation process with test specifications. Source: [2] 134

4 The two graphics describe the procedure of the method, however, the syntax is not defined; the ordinal scale is not defined; the origin of some values is not known and the format of such values are not standardized, finally, the concepts in two graphics are not connected Test-suite-execution-vector-based estimation This approach is based on the fact that: Similar tests have similar cost [14]. First, test suite should be categorized by its attributes. Then, the similarity between a candidate and a definite test suite execution vector in the historical database is determined. After that, the estimation effort is changed to reflect an algorithm to predict the effort value for a given vector. Fig. 7 shows the tester rank model used in the approach and the Fig. 8 shows a big picture of the approach. The graph uses the standard representation for its elements, but it is so general and a computational representation is difficult because all the information needed to estimate the effort is not present. Figure 9. Architecture of the proposed system. Source: [15] 3.3. Testing-activity-based estimation In this approach, we understand the project as a set of activities to be executed in the software development process Pre-coding- and post-coding-based estimation Figure 7. Tester rank. Source: [14] The architecture of the model involves two components: pre- and post-effort estimation components and a learning rule, which is used as back propagation algorithm [15]. In Fig. 9 this approach is shown. The graphical representation is close to the controlled language because the structure is similar to the block diagram. However, the syntax used is not defined, the symbols are used in others context but the interpretation cannot be made directly for this context, and the concepts in the boxes are not specified Three-phase-based estimation The testing effort needed to assure the permissible number of field defects is estimated from effort needed for design and review activities in three phases: investigation of the effect of the number of field defects on effort, construction of a regression model for the number of field defects, and estimation of testing effort to assure field quality [16]. Fig. 10 shows a schema for defect injection and defect removal. Such a schema shows the defect evolution according to each phase, but the interpretation is difficult because the syntax is not present. The main concepts proposed in the method and their relationships are not present. The representation fails to clarify the estimation of the testing effort. Figure 8. TSEV approach. Source: [14] 135

5 4. Knowledge representation of the process of estimation effort Figure 10. Defects injected and removed process. Source: [16] A state-of-the-art review of graphical representations of the testing effort estimation process was presented in Section 3. Based on this review, we can determine the different methods for estimating test effort and introduce several concepts affecting the measure. Several concepts introduced for the test effort process increases complexity of the representation of such concepts. Additionally, the scope of the graphical representations presented is broad with low specificity or so specific so that it is not possible to determine the relationship between the test effort process with actors, artifacts or any concept involved. In this Section, we integrate the concepts of the effort estimation methods previously studied. The representation is created by using a pre-conceptual schema. We aim to express the concepts in a formal way but maintaining proximity with natural language. Figure 11. Knowledge representation about testing effort in pre-conceptual schemas. Source: the authors. In the knowledge related to the test effort process estimation, we identify the following elements from the state of the art: The effort estimation methods are based on some specification: use case, requirements specifications, or test specification. The factors represent the attributes of the conceptrelated specifications. Each factor is associated with a direct metric. Some methods can use the same factor of the same specification, but the way to measure it makes differences between methods. For example, test execution complexity is a factor in test-specificationbased estimation; such a factor represents the difficulty of interaction between the tester and the tested product [17]. Test execution is a factor in test- 136

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