COMBINING INFORMATION RETRIEVAL MODULES AND ...

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ACKNOWLEDGMENTS I would like to thank my advisor Dr. Smith for providing me the opportunity to be in this PhD program. I appreciate his help, guidance and patience for me to complete the degree. I would also like to thank my co-advisor Dr. Kraft for his help. His intelligent ideas, writing skills, advices and patience are great help for me. Furthermore, I would like to thank my committee members: Dr. Atkison, Dr. Carver and Dr. Parrish for their guidance and advices to my dissertation and research. This research would not have been possible without the support from my family and friends. Finally I thank all faculty, staff and students at the University of Alabama who helped with my study and research. vii
CONTENTS ABSTRACT ................................................................................................ ii LIST OF ABBREVIATIONS <strong>AND</strong> SYMBOLS ...................................... iii ACKNOWLEDGMENTS ........................................................................ vii LIST OF TABLES .................................................................................... xii LIST OF FIGURES ...................................................................................xv 1. INTRODUCTION ...................................................................................1 2. BACKGROUND .....................................................................................5 2.1 Information Retrieval Techniques .........................................................5 2.1.1 Probabilistic Module ...........................................................................6 2.1.2 Vector Space Module ..........................................................................9 2.2 IR Applications ....................................................................................10 2.2.1 Program comprehension ...................................................................10 2.2.2 Impact analysis..................................................................................12 2.2.3 Traceability Recovery .......................................................................14 2.2.4 Reuse .................................................................................................15 2.2.5 Concept Location ..............................................................................15 2.2.6 Other Applications ............................................................................19 3. RELATED WORK ................................................................................22 3.1 Bug localization ...................................................................................22 viii
Page 1 and 2: COMBINING INFORMATION RETRIEVAL MOD
Page 3 and 4: ABSTRACT Bug localization and featu
Page 5 and 6: FCA Formal concept analysis: a tech
Page 7: S A diagonal matrix whose diagonal
Page 11 and 12: 5.4.2 Rhino .......................
Page 13 and 14: LIST OF TABLES 5.1 Three projects i
Page 15 and 16: 5.31 Rankings for six bugs in jEdit
Page 17 and 18: 5.10 Average Rankings for Rhino (al
Page 19 and 20: al., 2007; Shao et al., 2009), are
Page 21 and 22: 25 features in JavaHMO, 35 bugs in
Page 23 and 24: Ndqr Recall = Where Ndqr is the tot
Page 25 and 26: kβ Then Pr(wk |Di) = λ if wk
Page 27 and 28: probabilistic model obtained higher
Page 29 and 30: order to improve the quality of top
Page 31 and 32: software change artifacts. It conve
Page 33 and 34: e an object in object-oriented prog
Page 35 and 36: approaches to monitor, plan, and pr
Page 37 and 38: into situations where obtaining dir
Page 39 and 40: CHAPTER 3 RELATED WORK Chapter 3 gi
Page 41 and 42: integrate the LSI score (SLSI) and
Page 43 and 44: • Stemming - strip suffixes to re
Page 45 and 46: stretching and minimum squeezing th
Page 47 and 48: different types of software artifac
Page 49 and 50: the text description of software ar
Page 51 and 52: provided a useful first impression
Page 53 and 54: CHAPTER 4 RESEARCH METHOD Chapter 4
Page 55 and 56: 2. Construct and query the LSI mode
Page 57 and 58: those methods may also be relevant
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4.3 LSICG implementation LSICG comp
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called methods. Because the JDT is
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Question: How accurate are LSICG an
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classes identified for jEdit featur
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6. Play your MP3 files and streamin
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To accommodate the two change reque
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For a given feature, we first run L
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Likewise, we compute the average ra
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(NativeRegExp, emitREBytecode) 2639
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Figure 6 illustrates the overall re
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As was the case for JavaHMO, seven
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Figure 5.5 Average rankings from ea
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For each query in Table 5.13, we ru
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For JavaHMO and Rhino, we also prov
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5.5.2 Rhino We use all 35 bugs in R
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5.6 All bugs/features Results This
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plugins.organizer.OrganizerContaine
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900 800 700 600 500 400 300 200 100
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overload] 253323 Assignment to vari
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Table 5.21 Rankings of thirty-five
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well for small size of subjects (si
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254915 289 80 109 8 125 8 8 255549
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Pair 1 LSICG - LSI Mean Std. Deviat
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The average ranking of BestLSI is 2
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900 800 700 600 500 400 300 200 100
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Pair 1 Pair 1 BestofALL_ LSICG Best
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200 150 100 50 0 Figure 5.13 Averag
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pathMenu path_menu 1607211 Q51 jEdi
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Note that both BestLSI and BestLSIC
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5.6.4 Discussion of results The sec
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Table 5.32 Features and targeted cl
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Table 5.33 Bugs and targeted classe
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Table 5.34 Features and targeted cl
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Table 5.36 Rankings for JavaHMO (cl
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35 30 25 20 15 10 5 0 Figure 5.18 A
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256339 62 5 59 1 55 1 256575 28 5 1
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120 100 80 60 40 20 0 98 80 71 Figu
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For LSICG16 and LSI16, |t| = 5.608,
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For bug localization task in jEdit,
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70 60 50 40 30 20 10 Figure 5.22 Av
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CHAPTER 6 DISCUSSION The results of
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more coupling than the other two pr
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4. The projects in this study range
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call-away rule, try combining struc
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Canfora, G. and Cerulo, L. (2006a),
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Fry, Z. P. and Shepherd, D. and Hil
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Lewis, D., In Ifs, Harper, W.L., St
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Marcus, A. and Poshyvanyk, D. and F
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Websites JavaHMO, http://JavaHMO.so
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offset timezone] (NativeDate, date_
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254778 Rhino treats label as separa
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Parser,argumentList Parser,assignEx
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Interpreter,updateLineNumber Interp
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