Managing requirements conflicts in software product lines: A goal and scenario based approach

The product line approach is recognized as a successful approach to reuse in software development. However, in many cases, it has resulted in interactions between requirements and/or features. Interaction detection, especially conflict detection between requirements has become more challenging. Thus, detecting conflicts between requirements is essential for successful product line development. Formal methods have been proposed to address this problem, however, they are hard to understand by non-experts and are limited to restricted domains. In addition, there is no overall process that covers all the steps for managing conflicts. We propose an approach for systematically identifying and managing requirements conflicts, which is based on requirements partition in natural language and supported by a tool. To demonstrate its feasibility, the proposed approach has been applied to the home integration system (HIS) domain and the results are discussed.     

Synergy between Activity Theory and goal/scenario modeling for requirements elicitation,analysis, and evolution

ContextIt is challenging to develop comprehensive, consistent, analyzable requirements models for evolving requirements. This is particularly critical for certain highly interactive types of socio-technical systems that involve a wide range of stakeholders with disparate backgrounds; system success is often dependent on how well local social constraints are addressed in system design.ObjectiveThis paper describes feasibility research, combining a holistic social system perspective provided by Activity Theory (AT), a psychological paradigm, with existing system development methodologies and tools, specifically goal and scenario modeling.MethodAT is used to understand the relationships between a system, its stakeholders, and the system’s evolving context. The User Requirements Notation (URN) is used to produce rigorous, analyzable specifications combining goal and scenario models. First, an AT language was developed constraining the framework for automation, second consistency heuristics were developed for constructing and analyzing combined AT/URN models, third a combined AT/URN methodology was developed, and consequently applied to a proof-of-concept system.ResultsAn AT language with limited tool support was developed, as was a combined AT/URN methodology. This methodology was applied to an evolving disease management system to demonstrate the feasibility of adapting AT for use in system development with existing methodologies and tools. Bi-directional transformations between the languages allow proposed changes in system design to be propagated to AT models for use in stakeholder discussions regarding system evolution.ConclusionsThe AT framework can be constrained for use in requirements elicitation and combined with URN tools to provide system designs that include social system perspectives. The developed AT/URN methodology can help engineers to track the impact on system design due to requirement changes triggered by changes in the system’s social context. The methodology also allows engineers to assess the impact of proposed system design changes on the social elements of the system context.     

A study of project selection and feature weighting for analogy based software cost estimation

A number of software cost estimation methods have been presented in literature over the past decades. Analogy based estimation (ABE), which is essentially a case based reasoning (CBR) approach, is one of the most popular techniques. In order to improve the performance of ABE, many previous studies proposed effective approaches to optimize the weights of the project features (feature weighting) in its similarity function. However, ABE is still criticized for the low prediction accuracy, the large memory requirement, and the expensive computation cost. To alleviate these drawbacks, in this paper we propose the project selection technique for ABE (PSABE) which reduces the whole project base into a small subset that consist only of representative projects. Moreover, PSABE is combined with the feature weighting to form FWPSABE for a further improvement of ABE. The proposed methods are validated on four datasets (two real-world sets and two artificial sets) and compared with conventional ABE, feature weighted ABE (FWABE), and machine learning methods. The promising results indicate that project selection technique could significantly improve analogy based models for software cost estimation.     

A metric for rating the effectiveness of industrial automation systems using a derivative of function point analysis

This paper describes the on-going design of a factory automation performance and satisfaction metric that is based on variations to the function point analysis (FPA) algorithm used in software engineering and analytical hierarchy process methodology common to systems engineering. The paper asserts that a satisfaction rating can be obtained for completed automation projects based on this tautology. The metric uses five high-level functional ratings, which are subdivided according to complexity criteria. The value of the metric is modified by calculating the effect of 14 appropriate adjustment factors. A level of configurability is added by weighting these factors to suit any specific installation. The scores and configuration data are elicited by averaging results obtained from a multi-part survey administered to four or five company employees selected from a wide range of job titles and responsibilities. A simple implementation of the algorithm was completed and an extension of the work will provide an Internet-based version and a diagnostic tool for company use.     

Development of a fuzzy goal programming model for optimization of lead time and cost in an overlapped product development project using a Gaussian Adaptive Particle Swarm Optimization-based approach

The aim of this paper is to present a model-based methodology to estimate the optimal amount of overlapping and communication policy with a view to minimizing product development lead time and cost. In the first step of methodology, the underlying two factors are considered in order to formulate mathematically a multi-objective function for a complete product development project. To add these objectives, incommensurate in nature, a fuzzy goal programming-based approach is adopted as the second step. In order to attain the optimal solution of formulated objective function, this paper introduces a novel approach, “Gaussian Adaptive Particle Swarm Optimization” (GA-PSO), which is embedded with two beneficial attributes: (1) Gaussian probability distribution, and (2) Time-Varying Acceleration Coefficients strategy. An illustrative hypothetical example of mobile phones is detailed to demonstrate the proposed model-based methodology. Experiments are performed on an underlying example, and computational results are reported to support the efficacy of the proposed model.