A Size Metric-Based Effort Estimation Method For Service Oriented Architecture Systems

ABSTRACT

Service Oriented Architecture (SOA) is one of the recent software development paradigms

that enable alignment of business processes into integrated services within and outside

organizations regardless of the heterogeneity of technologies used. Determining the scope,

effort and cost of SOA systems is important to facilitate the planning and eventually

successful implementation of software projects. A number of methods have been proposed

to estimate effort of building SOA projects. Despite the fact that these methods are

promising, the problem of measuring SOA size and estimating SOA effort still remains

largely unresolved mainly because there is limited attempt in using Unified Modeling

Language (UML) size metrics to define size-based attributes for estimating SOA

development effort. To address this problem, a set of size metrics were defined and effort

estimation method that is based on the size metrics was developed. To automate the

computation of the metric and the method, a static analysis tool that uses deep learning

techniques to detect UML arrows and recognize text was constructed. The automated tool

deep learning techniques were each subjected to validity checks based on datasets of 100

operation names and 100 arrow head images. Briand’s theoretical validation was used to

test the validity of the designed size metrics and they were found to be mathematically

sound. Experimental research design was employed to sampled SOA systems to test

variables used in the study and the accuracy of the proposed effort estimation method and

implementation automated tool. A survey involving experts from the industry was carried

out to replicate and validate the experiment done by students and to determine the

appropriateness of the proposed size metrics, SOA development effort factors and the

implementation automated tool. The experiment was based on a sample of 15 students’

SOA projects developed by Meru University of Science and Technology students while the

survey involved 20 programmers from the industry. Descriptive statistics such as Mean

magnitude of relative error (MMRE) and Magnitude of Error (MRE) were used to test SOA

effort estimation accuracy while linear regression analysis tested relationship among

variables identified in the study. Result from the experiment revealed that the proposed

metrics and method are more accurate and there is a correlation between size attributes and

SOA size and between SOA size and SOA development effort. Response from the survey

showed that the proposed metrics and effort factors are valid and they have influence on

size and effort respectively. Findings from this study were meant to provide a basis for

future software engineering researchers to develop more effective and more accurate size

metrics and effort estimation methods.