Pre proof reading

chapters/ch01_introduction.tex

@@ -107,7 +107,10 @@ \subsection{Spectrum-Based Fault Localization}
 
 		\textbf{N$_{CS}$}: number of passed test cases that execute/cover the element
 
-		\textbf{N$_{US}$}: number of passed test cases that do not execute/cover the element
+		\textbf{N$_{US}$}: number of passed test cases that do not execute/cover
+		the element
+
+		Where an element is a unit of code such as a statement or a block.
 		\includegraphics[width=7cm]{contingency_table.png}
 		\caption{\label{fig:contingency_table} Variables in SBFL}
 	\end{center}
@@ -119,8 +122,6 @@ \subsection{Spectrum-Based Fault Localization}
 equation choices is further discussed in Related Works, however, this section
 will give an overview of how each looks like.
 
-% TODO: clearly define element here
-
 \subsubsection{Tarantula}
 \label{subsubsec:Tarantula}
 \begin{figure}[!htb]
@@ -170,8 +171,6 @@ \subsubsection{DStar}
 weight to \textbf{N$_{CF}$} and can be set to any number. However, the
 researchers suggested that * is set to 2 or 3.
 
-% TODO: create personal example and develop an explanation for it with
-% individual examples of plugging in data
 \subsection{SBFL in Action}
 \label{subsec:SBFLinAction}
 
@@ -183,12 +182,12 @@ \subsection{SBFL in Action}
 \end{figure}
 
 With the discussed equations and techniques to calculate suspiciousness in mind,
-the following example will demonstrate and discuss how AFLuent will utilize unit
+the following example demonstrates and discusses how AFLuent utilizes unit
 test and coverage data to locate faults in a program.
 Figure \ref{fig:sbfl_example}
 shows a sample python program to find the middle value
-from three integers. The program contains a bug on line 9 where the wrong middle
-number is detected. The figure also shows six different test cases that pass
+from three integers. The program contains a bug on line 6 where the wrong maximum
+value is detected. The figure also shows seven different test cases that send
 various inputs to the function and check whether the actual output matches the
 expected. The results of each test is found on the last row of the table.
 Additionally, the large dots under the Input Tests column illustrate the concept
@@ -199,6 +198,19 @@ \subsection{SBFL in Action}
 highlighted in warmer colors. Overall, all three equations are able to detect
 that line 9 is the most suspicious and is likely the cause of the test failure.
 
+The suspiciousness were calculated for each line by plugging values into the
+equations shown on Figures \ref{fig:tarantulaEquation},
+\ref{fig:ochiaiEquation}, and \ref{fig:dstarEquation}. For example, the scores
+for line 5 were calculated as follows:
+
+\begin{center}
+	\textbf{$Tarantula(5) = \frac{\frac{1}{1}}{\frac{6}{6} + \frac{1}{1}}$}
+
+	\textbf{$Ochiai(5) = \frac{1}{\sqrt{(1 + 0) \cdot (1 + 6)}} = \frac{1}{\sqrt{7}}$}
+
+	\textbf{$DStar(5) = \frac{1^3}{6 + 0}$}
+\end{center}
+
 \subsection{SBFL Criticism}
 \label{subsec:Criticism}
 

chapters/ch02_relatedwork.tex

@@ -34,7 +34,12 @@ \section{Automated Fault Localization}
 and reviews key literature that contributes show the benefits and drawbacks of
 each approach.
 
-% TODO: talk about the other lit review
+Another insightful survey paper is by Idrees Sarhan et. al \cite{sarhan2022Challenges}
+where he analyzes the main challenges in SBFL and some of
+the major obstacles that other literature attempts to tackle. AFLuent addresses
+two of the issues listed in this survey study, specifically element ties and
+division by zero. But many other challenges such as test flakiness, single vs.
+multiple bugs, and many others remain unaddressed.
 
 \subsection{SBFL Approaches}
 \label{subsec:sbfl}
@@ -320,7 +325,6 @@ \section{Existing Tools}
 	\end{center}
 \end{figure}
 
-% TODO: requires more expansion
 \section{Usability and Accessibility}
 \label{sec:usability_accessibility}
 
@@ -358,6 +362,3 @@ \section{Usability and Accessibility}
 very valuable to incorporate in AFLuent, especially that AFLuent is only able to
 conclude that there is an error and where it's possibly located. PABLO provides
 another layer of fault localization that can only enhance the user experience.
-
-% TODO: section about complexity metrics and tie breaking is needed with
-% extensive literature review

chapters/ch03_method.tex

@@ -661,8 +661,8 @@ \subsection{File Report}
 
 Aside from the reports produced in the console, AFLuent can create report files
 of the rankings in both JSON and CSV formats. Additional reporting of per-test
-coverage is also possible. The command line arguments \code{–-report} and
-\code{–-per-test-report} enable this feature. Producing these reports facilitates
+coverage is also possible. The command line arguments \code{--report} and
+\code{--per-test-report} enable this feature. Producing these reports facilitates
 the evaluation of AFLuent by allowing the storage of rankings in a file for
 further analysis later on. In general, the standard report contains the rankings
 of the lines using the approach requested by the user. Additionally, other
@@ -696,7 +696,8 @@ \subsection{Cyclomatic Complexity}
 
 One of the available ways to break ties between elements is to use cyclomatic
 complexity as a secondary score to consider when sorting. This score is proposed
-by McCabe \cite{cyclomatic_complexity} and can be easily calculated using the Radon library. It measures the number of available paths that execution
+by McCabe \cite{cyclomatic_complexity} and can be easily calculated using the Radon library.
+It measures the number of available paths that execution
 cold go through in a function. Since this type of score only applies to whole
 functions and not to individual elements, lines inherit the cyclomatic
 complexity score of the function they live in when being ranked.Essentially, if
@@ -710,7 +711,7 @@ \subsection{Mutant Density: Logical Set}
 \label{subsec:tiebreak_mutant_density_logical}
 
 Another metric used to break ties between suspicious statements is mutant
-density. More specifically, this score indicates how error prone the statement
+density \cite{Parsai_2020}. More specifically, this score indicates how error prone the statement
 is by calculating the number of all possible mutants. For example, a statement
 that has many mathematical and logical operators to perform a calculation is
 more error prone that a statement that only has one or two of these operations.

preamble/bibliography.bib

@@ -237,4 +237,26 @@ @INPROCEEDINGS{exam_scores
   pages={42-51},
   doi={10.1109/ICST.2008.65}}
 
-  @ARTICLE{cyclomatic_complexity,  author={McCabe, T.J.},  journal={IEEE Transactions on Software Engineering},   title={A Complexity Measure},   year={1976},  volume={SE-2},  number={4},  pages={308-320},  doi={10.1109/TSE.1976.233837}}
+@ARTICLE{cyclomatic_complexity,  author={McCabe, T.J.},  journal={IEEE
+Transactions on Software Engineering},   title={A Complexity Measure},
+year={1976},  volume={SE-2},  number={4},  pages={308-320},
+doi={10.1109/TSE.1976.233837}}
+
+@article{sarhan2022Challenges,
+  title={A Survey of Challenges in Spectrum Based Software Fault Localization},
+  author={Sarhan, Qusay I and Besz{\'e}des, {\'A}rp{\'a}d},
+  journal={IEEE Access},
+  year={2022},
+  publisher={IEEE}
+}
+
+@inproceedings{Parsai_2020,
+	doi = {10.1145/3387940.3392210},
+	url = {https://doi.org/10.1145%2F3387940.3392210},
+	year = 2020,
+	month = {jun},
+	publisher = {{ACM}},
+	author = {Ali Parsai and Serge Demeyer},
+	title = {Mutant Density},
+	booktitle = {Proceedings of the {IEEE}/{ACM} 42nd International Conference on Software Engineering Workshops}
+}