臺灣博碩士論文加值系統

軟體在偵錯與除錯階段時，隨著偵錯時間的增加軟體偵錯人員對於被測試軟體的熟悉度以及經驗也隨之增加。這些經由偵錯而取得的經驗將有助於測試人員去除剩餘在軟體內的錯誤，進而加速偵錯人員的除錯速度。所以為了更精確地描述軟體可靠度成長過程，軟體可靠度成長模型必須考慮測試人員在除錯階段的學習曲線。在現行的軟體可靠度工程中，軟體偵錯率(fault detection rate)是最常被用來測量學習曲線的指標。但是我們發現，在無限錯誤數模型中軟體偵錯率在任何時間點都等於零，此種現象使得軟體偵錯率無法被用來測量無限錯誤數模型中的學習曲線。
本論文提出一個方程式去描述測試人員學習曲線現象以及軟體可靠度成長之間的關係，此方程式可同時使用在有限錯誤數及無限錯誤數模型裡。藉由此方程式並且以Exponential c.d.f 以及 Weibull c.d.f.模擬測試人員之學習曲線，本論文提出兩個以非其次卜瓦松過程為基礎的新模型，並經由八組實際失效資料的測試，其測試結果顯示新模型有著較穩定的表現。

During software testing and debugging phase,testers accumulate experiences and become better bug-killers as testing progresses. This could result in accelerated reliability growth. In order to describe fault removal process more accurately, we have to consider learning phenomenon of testers during the testing phase. In the present software reliability engineering, we usually use fault detection rate to capture the leaning process in SRGMs and many proposed nonhomogeneous Poisson
process (NHPP) models suggest that learning usually manifests itself as a changing fault detection rate. However, we find that fault detection rate
can not be used to capture learning process in infinite fault models.
Motivated by correcting the drawbacks of fault detection rate, we propose a new equation to describe the relationship between learning process and fault removal process. Based on this equation, we propose two new NHPP models by incorporating Exponential c.d.f. and Weibull c.d.f. to model the learning process. Experiments on eight real datasets have been performed. The results show that the new models give a consistent fit performance.

摘要....................................................................I
ABSTRACT............................................................... II
致謝....................................................................III
TABLE OF CONTENTS...................................................... IV
LIST OF FIGURES........................................................ VI
LIST OF TABLES..........................................................VII
CHAPTER 1 INTRODUCTION..................................................1
CHAPTER 2 SOFTWARE RELIABILITY..........................................3
2.1 SOFTWARE RELIABILITY MODELS ..................................... 3
2.2 NON-HOMOGENEOUS POISSON PROCESS SRGMS............................ 4
2.3 LEARNING PHENOMENON IN SRGMS..................................... 7
2.4 FAULT EXPOSURE RATIO..............................................9
CHAPTER 3 LEARNING CURVE BASED SOFTWARE RELIABILITY MODELING ...........11
3.1 FAULT EXPOSURE PROCESS ...........................................11
3.2 FAULT REMOVAL PROCESS WITH LEARNING PHENOMENON................... 12
3.3 NEW THEOREM...................................................... 13
3.4 NEW MODELS....................................................... 16
3.4.1 The New Model with Weibull c.d.f..............................16
3.4.2 The New Model with Exponential c.d.f..........................17
3.5 PARAMETERS ESTIMATION ........................................... 18
3.6 APPLICATIONS TO REAL DATASETS.................................... 19
CHAPTER 4 MODEL EVALUATION..............................................23
4.1 COMPARISON CRITERION............................................. 23
4.2 THREE SELECTED EXISTING SRGMS FOR COMPARISON..................... 23
4.2.1 Goel-Okumoto model ...........................................23
4.2.2 Delayed S-shaped model........................................24
4.2.3 Musa-Okumoto Logarithmic Poisson model........................24
4.3 MODEL COMPARISON WITH REAL DATASETS.............................. 24
CHAPTER 5 CONCLUSIONS AND DISCUSSIONS...................................28
5.1 CONCLUSIONS...................................................... 28
5.2 DISCUSSIONS...................................................... 28
REFERENCE...............................................................30

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