Software Component Sequencing for Accurate Effort Estimation
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Solution Overview
Problem
Current effort estimation methodologies for software package implementation and customization are inadequate, as they fail to accurately bridge the gap between package capabilities and customization needs, rely heavily on project team technical knowledge, and do not account for the unique characteristics of package implementations, leading to inefficiencies and sub-optimal project planning.
Innovation Solution
A method that identifies and sequences components from a configuration table to prioritize execution of use cases, attributes complexity using a unit effort table, and considers usage types and reuse levels to determine effort estimation, while also performing gap analysis and detailed design preparation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional estimation methodologies are used for package implementation, then the estimation process is simple and quick, but the accuracy of effort estimation is poor and does not account for customization needs
Solution Approach 1:
The methodology segments the package implementation into distinct components: out-of-box features, customizations, and build-from-scratch developments. Each segment is estimated separately using appropriate techniques, then aggregated to provide an accurate overall effort estimation that accounts for the unique characteristics of package implementations.
Solution Approach 2:
The methodology performs preliminary gap analysis between package capabilities and customer requirements before final effort estimation. This preliminary action identifies customization needs and allows the estimation to account for these factors, improving accuracy while maintaining a structured approach.
2Measurement precision
If detailed gap analysis is performed between package capabilities and requirements, then the accuracy of customization estimation is improved, but the time required for estimation increases
Solution Approach 1:
The gap analysis is performed as a preliminary action that structures the estimation process. By conducting this analysis upfront and organizing findings into standardized categories (out-of-box, customization, build-from-scratch), the methodology improves accuracy while controlling time through structured progression rather than unstructured exploration.
Solution Approach 2:
The methodology changes the parameters of estimation by introducing specific metrics for package implementation (usage type, reuse level, customization depth) rather than using generic software estimation parameters. This allows more accurate customization estimation while the parameter structure guides the estimation process to be more efficient.
3Productivity
If task allocation is done at use case level without considering developer expertise, then the allocation process is simple, but the usage of available expertise is sub-optimal
Solution Approach 1:
The task allocation process is made dynamic by considering both use case requirements and individual developer expertise profiles. The allocation algorithm adapts to match developers with tasks based on their skill sets, improving productivity while the dynamic nature of the process allows it to handle complexity through intelligent matching rather than rigid rules.
Solution Approach 2:
The estimation methodology serves multiple functions: it estimates effort, identifies customization needs, and provides inputs for optimal task allocation. By making the methodology multi-functional, the system improves overall project planning efficiency without requiring separate specialized processes for each function.
4Adaptability or versatility
If existing estimation methodologies are used, then the process is standardized and easy to apply, but it does not bridge the gap between package offerings and customization needs
Solution Approach 1:
The methodology segments package implementation into standardized categories (out-of-box, customization, build-from-scratch) that can be systematically applied. This segmentation provides adaptability to different package scenarios while maintaining ease of application through standardized classification and estimation procedures for each segment.
Solution Approach 2:
The methodology introduces an intermediary gap analysis step that bridges the gap between package capabilities and customer requirements. This intermediary process translates package offerings into estimated effort while maintaining ease of application through structured analysis procedures that guide the estimator through the bridging process.
Data Source
AI summary
A method for improving execution efficiency of a software package customization is disclosed. The method includes identifying one or more components from a configuration table to implement at least one of a use case flow or a non functional requirement (NFR) or an interface document or combinations thereof derived using an use case of the software project, sequencing the identified one or more components to prioritize execution of the use case of the software project and attributing complexity of the identified one or more components to determine the effort estimation for execution of the use case. Attributing complexity includes using an unit effort table for determining the effort estimation requirement for execution of each component of the use case. The method further includes identifying a usage type, attributing the reuse level and resolving the dependencies among the identified components.