Tom Wanyama

Towards providing decision support for COTS selection

The evolution of software engineering has led to component-based software development, which in turn has engendered tremendous interest in the development of plug-and-play reusable software, leading to the concept of commercial off-the-shelf (COTS) software components. The use of COTS is increasingly becoming commonplace. This is mainly due to shrinking budgets, accelerating rates of COTS enhancement, development time and effort constraints, and expanding system requirements. However, the process of selecting COTS products is characterized by a multiplicity of challenges, which should be addressed in order to harness the benefits of COTS-based software development. In this paper we preset a model that splits the COTS selection process into layers; basing on the (intra-layer) activities which affect the choice of a decision support to address a particular challenge. Moreover, we evaluate the COTS selection methods in the reviewed literature according to how they address the challenges. Finally we present the functionalities of an ideal decision support system (DSS) for COTS selection, as well as the techniques for achieving the functionalities

Metrics for agent-based software development

In software engineering community an increasing effort has been put into design and development of multiagent systems (MAS). However, agent system development is currently dominated by informal guidelines, heuristics and inspirations rather than formal principles and well defined engineering techniques. In this paper we define a set of objective and subjective metrics to measure the complexity of MAS. The subjective metrics is a modified version of function point (FP) including the algorithmic complexity and knowledge complexity factor. The objective metrics is a measure for nearly-decomposability, measured by the communicative cohesion. Such metrics can be used to select the best architecture for the MAS. A methodology for agent-based software development based on such metrics is proposed.

Negotiation coalitions in group-choice multi-agent systems

In Group-Choice Decision Making (GCDM) where a number of stakeholders are involved in choosing a single solution from a set of available solution options, it is common for the stakeholders to form coalitions during negotiations in order to increase their individual welfare. It is also common to use Multi-Agent Systems (MAS) to automate GCDM processes. In such MAS, agents have to form coalitions like their human counterparts. Within each coalition, the individual agents behave according to the strategies of their clients. In this paper we present a coalition formation model which can employ multiple coalition formation algorithms. The model is motivated by a real-world group-choice multi-agent system that we developed; in the system, agents can form coalitions dynamically based on the similarity between their preferences, and based on the ownership criteria. Finally, this paper presents simulation results that illustrate the operational effectiveness of our coalition formation model.

A Multi-Agent Framework for Conflict Analysis and Negotiation: Case of COTS Selection

The process of evaluating and selecting Commercial Off-The-Shelf (COTS) products is complicated because of conflicting priorities of the stakeholders, complex interdependences among the evaluation criteria, multiple evaluation objectives, changing system requirements, and a large number of similar COTS products with extreme capability differences. Numerous COTS evaluation and selection methods have been proposed to address the complexity of the process. Some of these methods have been successfully applied in industry. However, negotiation to resolve stakeholder conflicts is still an ad hoc process. In this paper, we present a systematic model that assists the COTS selection stakeholders in identifying conflicts, as well as in determining and evaluating conflict resolution options. Our model is facilitated by an agent-based decision support system, which has user agents that assist the stakeholders in the COTS evaluation and negotiation process. The agents utilize a hybrid of analytic and artificial intelligence techniques to identify conflicts and the corresponding agreement options. Moreover, each user agent analyzes the agreement options in detail before advising its client about which goals to optimize, and which goals to compromise in order to reach agreement with the other stakeholders. Finally, the community of agents facilitates information sharing among stakeholders in a bid to improve the quality of their COTS selection decisions.

COTS Evaluation Supported by Knowledge Bases

Selection of Commercial-off-The-Shelf (COTS) software products is a knowledge-intensive process. In this paper, we show how knowledge bases can be used to facilitate the COTS selection process. We propose a conceptual model to support decision makers during the evaluation procedures. We then describe how this model is implemented using agent technologies supported by two knowledge bases (KB): the COTS KB and the methods KB. The model relies on group-decision making and facilitated stakeholder negotiations during the selection process. It employs hybrid techniques, such as Bayesian Belief Networks and Game Theory, to address different challenges throughout the process. In addition, the paper also describes how the COTS knowledge base can be used at three levels of usage: global (over the internet), limited (between limited number of organizations) and local (within a single organization).

An Expert System for Diagnosing Heavy-Duty Diesel Engine faults

Heavy-Duty Diesel Engines (HDDEs) support critical and high cost services, thus failure of such engines can have serious economic and health impacts. It is necessary that diagnosis is done during both preventive maintenance and when the engine has failed. Because of their complexity, HDDEs require high expertise for the diagnosis of their faults; such expertise is in many cases scarce, or just unavailable. Current computerized tools for diagnosing HDDEs are tied to particular manufacturer’s products. In addition, most of them do not have the functionality that is required to assist inexperienced technicians to completely diagnose and repair HDDE faults, because most of the tools have only the capability to identify HDDE faults. These tools are not able to recommend corrective action. This paper presents an easy to use expert system for diagnosing HDDE faults that is based on the Bayesian Network Technology. Using Bayesian Networks simplified the modeling of the complex process of diagnosing HDDEs. Moreover, it enabled us to capture the uncertainty associated with engine diagnosis, and to incorporate learning capabilities in the expert system.

Repositories for Cots Selection

Selecting commercial-off-the-shelf (COTS) products is a challenging process that utilizes and generates a lot of information. Repositories play a crucial role in the management of the COTS selection information. In fact, it is generally believed in literature that repositories are of great importance to the COTS selection process and indeed to the entire process of developing software using COTS products. However, the process of developing, managing, and accessing these repositories has attracted very little attention. This paper presents a framework for establishing and maintaining the following five different repositories for the COTS selection process: COTS repository, user repository, discussions repository, lessons-learned repository, and historical information repository. The framework supports distributed contribution and access to the repositories, as well as systematic and hierarchical evaluation and integration of the contributions. Moreover, this paper presents a description of a database that was implemented as part of a decision support system (DSS) for the selection of COTS products. The database accommodates the different repositories for the COTS selection process

Static and Dynamic Coalition Formation in Group-Choice Decision Making

In Group-Choice Decision Making (GCDM) where a number of stakeholders are involved in choosing a single solution from a set of available solution options, it is common for the stakeholders to form coalition during negotiation in order to increase their individual welfare. It is also common to use Multi-Agent Systems (MAS) to automate GCDM processes. In such MAS, agents have to form coalitions like their human counterparts, and within each coalition, the individual agents behave according to the strategies of their clients. This paper presents a coalition formation engine that has two coalition formation algorithms. One of the algorithms is based on the concept of static coalition formation, and the other is based on the concept of dynamic coalition formation. Moreover, the coalition formation engine is coupled with algorithms that govern the social behavior of the agents in their coalitions, to form an agent negotiation engine. Finally, this paper presents an example and simulation results that illustrate the operational effectiveness of the two coalition formation algorithms, as well as the algorithms that govern the social behavior of the agents.

A Negotiation Model for Large Scale Multi-Agent Systems

Modeling agent negotiation is of key importance in building multiagent systems. Negotiation provides the basis for managing the expectations of the individual negotiating agents, and it enables selecting solutions that satisfy the agents as much as possible. Thus far, most of the negotiation models have limitations when employed in large scale multiagent systems. This paper presents a negotiation model for large scale multiagents systems that is based on qualitative reasoning (QR) and game theory (GT). In the model, each agent classifies its negotiation opponents according to the similarity of their preference model. The agents use QR components of the model to estimate the preference models of their opponents, and to determine the amount of tradeoff associated with the various solution options. Moreover, they use the GT component of the model to determine the social-acceptance of each of the solution options. The output of the QR and GT components of the negotiation model is used to determine the rationale for accepting or rejecting offers made by the opponents

Qualitative reasoning model for tradeoff analysis

In Multi-Criteria Decision Making problems such as choosing a development policy, selecting software products, or searching for commodities to purchase, it is often necessary to evaluate solution options in respect of multiple objectives. The solution alternative that performs best in all the objectives is the dominant solution, and it should be selected to solve the problem. However, usually the selection objectives are incomparable and conflicting, making it impossible to have a dominant solution among the alternatives. In such cases, tradeoff analysis is required to identify the objectives that can be optimized, and those that can be comprised in order to choose a winning solution. In this paper we present a tradeoff analysis model based on the principles of qualitative reasoning that provides visualization support for understanding interaction and tradeoff dependences among solutions evaluation criteria which affect the tradeoff among selection objectives. Moreover, the decision support system based on our tradeoff analysis model facilitates discovery of hidden solution features so as to improve the completeness and certainty of the user preference model.