Probabilistic Models for Software Quality Analysis

機率式的軟體品質分析模式

10.29977/JCIIE.200607.0007

王鄭慈(Cheng-Tzu Wang)；駱至中(Chih-Chung Lo)；簡添福(Tien-Fu Jean)

軟體品質 ； 軟體衡量 ； 堆疊基馬可夫程序 ； software quality ； software measurement ； stack-based Markov process

工業工程學刊

23卷4期（2006 / 07 / 01）

328 - 336

英文

在軟體工程領域中如何改善軟體的品質是一個重要的議題。因此，在開發過程中，軟體工程師必須要能儘早掌握軟體的品質資訊，方能有效提昇並確保軟體品質。在這篇論文中，我們根據不同的衡量觀點，提出五種不同的堆疊基馬可夫模式來評估軟體複雜度，論文中說明並分析這些模式與軟體品質之間的關係。實證結果顯示，本論文所提出的堆疊基馬可夫模式與軟體品質有正向關係。所以，這些研究結果可以幫助開發者在軟體開發過程的初期就可由系統程式碼中分析及預測軟體的品質狀況，進而找出其間問題所在並加以改良，資訊系統的軟體品質也因此獲得改善。

Improving the qualities of software systems is an important issue in software engineering. Software engineers need to know the information about software quality as early as possible in software development. Measuring the program complexity of software systems is the most straightforward approach to analyze software quality. In his paper, we propose five Stack-based Markov (SBM) models to measure program complexity from different viewpoints. Relationships between each proposed SBM model and software quality are investigated. Empirical study reveals that these SBM models have positive impact on software quality factors. These findings could help software development organizations to predict and control their software quality in the early stage of software development.

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