Research On Software Test Effort Measurement In Enhancement Projects

Over the past decades, software test is increasingly growing. Meanwhile, as a large volume of software products are generated, enhancement projects to support evolution of exsiting software become the majority in software industry instead of creating brand-new software. Thus, it is of key value to provide effective solutions to test effort measurement in enhancement projects. However, only few published researches and practices in effort measurement field aim to address either enhancement projects or software test.This paper presents research on methodoloies and technologies of software test effort measurement in enhancement projects, and it covers test sizing, test activity effort estimation, test task effort estimation, and uncertainty measurement.The major contributions of the paper are presented as below, including:Firstly, it proposes a new test size metric called test verification point (TVP) to derive the number of test case (TCN) at early stage of a project. A test object tree is created to identify and organize test objects of an application, and then TVP is derived from the count of functional rules attach to test objects. After that, TCN can be estimated from TVP since the metric is generated from the view of test objectives.Secondly, it proposes a binary sizing model with increment size (INC) and appendix size (APPD) to cover both new added or updated functions and existing functions for regression test. The relationships between the binary size with TCN metrics and test effort for test design activity, test execution activity and test support activity are respectively analyzed and presented. Algorithms, like linear regression, adjusted average benchmark, and anology are introduced to measure effort of each test activity. At last, the total test effort can be derived from test design effort, test execution effort and test support effort.Thirdly, it proposes a test task vector model which contains size, complexity, and tester rank sub-models. For each test suite, the model analyzes its size and complexity, and measures the rank of a tester who is assigned to execute the task. Based on the experience database of test task verctors, algorithms, like multiple linear regression, anology, support vector machine regression, etc, are then inroduced to estimate effort of new vectors.Fourthly, it proposes an earned-value based uncertainty measurmenet model to deal with the uncertainty at different progress of a project. It introduces value-at-risk model from finance field to meaure the effort uncertainty in a software project, and combines earned-value feedback process to track uncertainties through the whole software life cycle to support project buffer management.Case studies indicate that models and approaches proposed above are effective and can provide accurate estimation of software test. Based on the practice of three enhancemnt projects, we find they are competitive against existing algorithm models and expert estimation methods from both applicability and accuracy.In general, this paper proposes a research framework of function test effort measurement on enhancement projects and provides a set of applicable solutions, and the work of the paper can be a good base for further researches and practices.