A Generic Quality Assessment Environment for ComponentBased Software Systems

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A Generic Quality Assessment Environment for
Component-Based Software Systems

• Cai Xia
• March 13, 2001

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Outline

• Introduction
• Objective and features
• Metrics and Models
• Prototype
• Conclusion

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Introduction

• Component-based software development (CBSD) is
  to build software systems using a combination of
  components
• The over quality of the final system greatly
  depends on the quality of the selected
  components.
• Software metrics are designed to measure
  different attributes of a software system and
  development process. E.g., process metrics,
  static code metrics and dynamic metric.

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Introduction

• several different techniques have been developed
  to describe the predictive relationship between
  software metrics and the classification of the
  software components into fault-prone and non
  fault-prone categories
• discriminant analysis
• classification tree
• pattern recognition
• Bayesian belief network (BBN)
• case-based reasoning (CBR)
• regression tree models

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Introduction

• There are also some prototype or tools that use
  such techniques to automate the procedure of
  software quality prediction.
• However, these tools address only one kind of
  metrics, and only one prediction technique for
  the overall software quality assessment.
• We propose a Component-based Program Analysis
  and Reliability Evaluation (ComPARE) to combine
  different metrics and models to evaluate the
  quality of software systems in CBSD.

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Architecture
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Objective

• To predict the overall quality by using process
  metrics, static code metrics as well as dynamic
  metrics.
• To integrate several quality prediction models
  into one environment and compare the prediction
  result of different models
• To define the quality prediction models
  interactively

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Objective

• To display quality of components by different
  categories
• To validate reliability models defined by user
  against real failure data
• To show the source code with potential problems
  at line-level granularity
• To adopt commercial tools in accessing software
  data related to quality attributes

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Progress and Dynamic Metrics
 
 
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Static Code Metrics
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Models

• Summation Model

• Product Model

mis are normalized as a value which is close to 1
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Models

• Classfication Tree Model
• classify the candidate components into different
  quality categories by constructing a tree
  structure

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Models

• Case-Based Reasoning
• A CBR classifier uses previous similar cases as
  the basis for the prediction. case base.
• The candidate component that has a similar
  structure to the components in the case base will
  inherit a similar quality level.
• Euclidean distance, z-score standardization, no
  weighting scheme, nearest neighbor.

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Models

• Bayesian Network
• a graphical network that represents probabilistic
  relationships among variables
• enable reasoning under uncertainty
• The foundation of Bayesian networks is the
  following theorem known as Bayes Theorem

where H, E, c are independent events, P is the
probability of such event under certain
circumstances
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BBN Example reliability prediction
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Prototype

• Selecting Metrics
• Selecting Criteria
• Model selection and definition
• Model Validation
• Display result (Display menu)

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Prototype

• GUI of ComPARE for metrics, criteria and tree
  model

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Prototype

• GUI of ComPARE for prediction display,
  risky source code and result statistics

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Conclusion

• Problem to certify the quality of individual
  components, as well as the overall system.
• ComPARE a generic quality assessment environment
  for software components.
• It collects metrics, integrates different
  reliability assessment models, and validates them
  against the failure data collected in real life.

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Q A