COMBINING INFORMATION RETRIEVAL MODULES AND ...

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and present the results of a study comparing manual RTM generation to RTM generation using RETRO. The study showed that RETRO found significantly more correct links than manual tracing and took only one third of the time to do so. Li et al. 2008 introduces a Requirement Centric Traceability (RCT) approach to analyze the change impact at the requirement level, as we talked about in Impact Analysis section. 2.2.4 Reuse Reuse is the use of existing software. The purpose of software reuse is to save time and energy by reducing redundant work. Etzkorn et al. 2001 described the analysis, in terms of quality factors related to reusability, contained in an approach that aids significantly in assessing existing OO software for reusability. An automated tool implementing the approach is validated by comparing the tool's quality determinations to that of human experts. The comparison provides insight into how OO software metrics should be interpreted in relation to the quality factors they purport to measure. Chang and Mockus 2008 evaluated a simple-to-use method that needs only a set of file pathnames to identify directories that share filenames and improve reuse detection at the file level. The authors applied the method and four additional file copy detection methods that utilize the underlying content of multiple versions of the source code on the FreeBSD project. Their tool extended the concept of copy detection to the comparison files having multiple versions and exemplified the methods and the validation process on FreeBSD CVS version repository. 2.2.5 Concept Location Concept Location is the process to locate the implementation of a concept in source code. The assumption of concept location is that the developers understand the concept, but do not know where in source code they are located. There are several definitions of concept. A concept could 15
e an object in object-oriented programs, or it could be a set of attributes in source code. Rajlich and Wilde 2002 defined concepts as units of human knowledge that can be processed by the human mind (short-term memory) in one instance. Etzkorn et al. 1996 addresses an approach to the automated understanding of object- oriented code and a knowledge-based system that implements the approach. The understanding approach worked fairly well. Keyword recognition has been the primary source of information, since the packages analyzed have all been fairly sparsely commented. Some false recognition does occur when only keywords from identifiers are available. Liu et al. 2001 and Liu et al. 2002 used intelligent search techniques to automate the process of transforming a query string, so that it will find suitable results, in the context of source code. These techniques include abbreviation expansion, abbreviation contraction, as well as techniques based on dictionary or knowledgebase lookup. Their methods are relatively simple to implement, and are shown to be useful to Software Engineers in preliminary experiments. Marcus and Maletic 2001 apply LSI to identifiers and comments to detect similar high- level concepts such as abstract data types. In a case study of Mosaic, the authors demonstrate that their approach outperforms a simple word matching method. Though their approach is easy to implement and has a low computational cost, it lacks some precision and is not well automated. Marcus et al. 2004 again applied LSI for concept location and their results show that LSI is almost as easy and flexible to use as grep based techniques and it provides better results, and LSI was able to identify certain parts of a concept that was missed by the dependence graph search approach. Marcus et al. 2005 further applied three different techniques: grep, Dependency Search, LSI, for concept location in Object-Oriented projects. They focused on static concept location techniques that share common prerequisites and are search the source code using regular 16
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 and 8: S A diagonal matrix whose diagonal
Page 9 and 10: CONTENTS ABSTRACT .................
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: software change artifacts. It conve
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
Page 59 and 60: 4.3 LSICG implementation LSICG comp
Page 61 and 62: called methods. Because the JDT is
Page 63 and 64: Question: How accurate are LSICG an
Page 65 and 66: classes identified for jEdit featur
Page 67 and 68: 6. Play your MP3 files and streamin
Page 69 and 70: To accommodate the two change reque
Page 71 and 72: For a given feature, we first run L
Page 73 and 74: Likewise, we compute the average ra
Page 75 and 76: (NativeRegExp, emitREBytecode) 2639
Page 77 and 78: Figure 6 illustrates the overall re
Page 79 and 80: As was the case for JavaHMO, seven
Page 81 and 82: Figure 5.5 Average rankings from ea
Page 83 and 84:
For each query in Table 5.13, we ru
Page 85 and 86:
For JavaHMO and Rhino, we also prov
Page 87 and 88:
5.5.2 Rhino We use all 35 bugs in R
Page 89 and 90:
5.6 All bugs/features Results This
Page 91 and 92:
plugins.organizer.OrganizerContaine
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900 800 700 600 500 400 300 200 100
Page 95 and 96:
overload] 253323 Assignment to vari
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Table 5.21 Rankings of thirty-five
Page 99 and 100:
well for small size of subjects (si
Page 101 and 102:
254915 289 80 109 8 125 8 8 255549
Page 103 and 104:
Pair 1 LSICG - LSI Mean Std. Deviat
Page 105 and 106:
The average ranking of BestLSI is 2
Page 107 and 108:
900 800 700 600 500 400 300 200 100
Page 109 and 110:
Pair 1 Pair 1 BestofALL_ LSICG Best
Page 111 and 112:
200 150 100 50 0 Figure 5.13 Averag
Page 113 and 114:
pathMenu path_menu 1607211 Q51 jEdi
Page 115 and 116:
Note that both BestLSI and BestLSIC
Page 117 and 118:
5.6.4 Discussion of results The sec
Page 119 and 120:
Table 5.32 Features and targeted cl
Page 121 and 122:
Table 5.33 Bugs and targeted classe
Page 123 and 124:
Table 5.34 Features and targeted cl
Page 125 and 126:
Table 5.36 Rankings for JavaHMO (cl
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35 30 25 20 15 10 5 0 Figure 5.18 A
Page 129 and 130:
256339 62 5 59 1 55 1 256575 28 5 1
Page 131 and 132:
120 100 80 60 40 20 0 98 80 71 Figu
Page 133 and 134:
For LSICG16 and LSI16, |t| = 5.608,
Page 135 and 136:
For bug localization task in jEdit,
Page 137 and 138:
70 60 50 40 30 20 10 Figure 5.22 Av
Page 139 and 140:
CHAPTER 6 DISCUSSION The results of
Page 141 and 142:
more coupling than the other two pr
Page 143 and 144:
4. The projects in this study range
Page 145 and 146:
call-away rule, try combining struc
Page 147 and 148:
Canfora, G. and Cerulo, L. (2006a),
Page 149 and 150:
Fry, Z. P. and Shepherd, D. and Hil
Page 151 and 152:
Lewis, D., In Ifs, Harper, W.L., St
Page 153 and 154:
Marcus, A. and Poshyvanyk, D. and F
Page 155 and 156:
Websites JavaHMO, http://JavaHMO.so
Page 157 and 158:
offset timezone] (NativeDate, date_
Page 159 and 160:
254778 Rhino treats label as separa
Page 161 and 162:
Parser,argumentList Parser,assignEx
Page 163:
Interpreter,updateLineNumber Interp
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