Integrating a Graph Builder into Python Tutor
Diogo Soares #
University of Minho, Braga Portugal
Maria João Varanda Pereira1 #Ñ
Research Centre in Digitalization and Intelligent Robotics,
Polythechnic Insitute of Bragança, Portugal
Pedro Rangel Henriques #Ñ
University of Minho, Braga, Portugal
Analysing unknown source code to comprehend it is quite hard and expensive task. Therefore, the
Program Comprehension (PC) subject has always been an area of interest as it helps to realize how
a program works by identifying the code that implements each functionality. This means being able
to map the problem domain with the program domain. PC is a complex area, but its importance for
programmers is so high that many approaches and tools were proposed along the last two decades.
Program Animation is one of those approaches requiring specialized techniques.
For each programming language, there are already tools that enable us to execute a program
step by step, visualize its execution path, observe the effect of each instruction on its data structures,
and inspect the value of its variables at any point.
In the present context, we sustain the idea that PC techniques and tools can also be of great value
for students taking the first steps in programming using a specific language. To this end, we aim to
improve Python Tutor, a well-known program visualization tool, with graph-based representations
of source code such as Control Flow Graph (CFG), Data Flow Graph (DFG), Function Call Graph
(FCG) and System Control Graph (SCG).
This helps novice programmers to understand the source code analyzing not only the variable
contents but also a set of automatically generated graph-based visualizations, that were not included
in Python Tutor so far. This will allow the students to be focused on certain aspects of the program
(depending on the graph), abstracting others such as details of its syntax.
2012 ACM Subject Classification Human-centered computing → Visualization systems and tools;
Social and professional topics → Computer science education; Software and its engineering → Source
Keywords and phrases Program Visualization, Python Tutor, Data Flow Graphs, Control Flow
Digital Object Identifier 10.4230/OASIcs.ICPEC.2021.6
Supplementary Material Software (Graph Builder):
Funding This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the
Projects Scopes: UIDB/05757/2020 and UIDB/00319/2020.
There’s along the web many tutorials on programming and several learning methodologies
but the majority of them uses the same formula : start with the explanation of a set of
examples and propose some similar exercises that train the students to write similar code to
solve them. To fully understand a programming language, it is not enough to read the code
to mark corresponding author
© Diogo Soares, Maria João Varanda Pereira, and Pedro Rangel Henriques;
licensed under Creative Commons License CC-BY 4.0
Second International Computer Programming Education Conference (ICPEC 2021).
Editors: Pedro Rangel Henriques, Filipe Portela, Ricardo Queirós, and Alberto Simões; Article No. 6; pp. 6:1–6:15
OpenAccess Series in Informatics
Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany
6:2 Graph-Based Visualization Builder
of programs written in that language, it is also necessary to comprehend how the computer
behaves when executing the programs code. For each piece of code, the student shall be able
to build a full map between the program domain and the problem domain.
Usually it is hard to understand his own code, even to a software engineer that has to
maintain third-part code he never saw before, the task is much harder. The average developer
spends about 60% to 90% of his time understanding code previously developed; these figures
are not changing over time .
With this in mind, this paper describes and discusses the integration into Python Tutor
of a new tool for automatic generation of graph-based visualizations to turn ease the task of
program comprehension. As said in , the best way to understand programs is by giving
programs another aspect than that of their source code. This new feature works as Python
Tutor plugin and it analyses the input code and produces several types of complementary
graphs depending on the user desires. As the program comprehension task is also very
relevant for the beginners wishing to understand how a given program works and how it
solves a given problem, we believe that Python Tutor improved with our plug-in will be very
useful to help programming students.
The paper has 5 sections after that. The state of the art on program animation appears in
Section 2. Section 3 discusses how graph-based visualizations can be included in an Animator
adding actual value to it. Section 4 exhibits the architecture to build the new tool and
discusses how it can be developed and integrated into the host tool. Section 5 illustrates
the final system built and describes a experiment conducted to assess the value added by
the visualizations provided. Section 6 closes the paper and points out some directions for
2 Program Visualization and Animation
Think-aloud protocol  is a method where a person verbalize his thoughts and it can be
used not only to improve self−understanding and reasoning but also to explain the cognitive
process to others. That’s why is so important to produce documentation when developing
software. The use of this method also allows realizing that the thoughts of a developer differ
according to the familiarity of a program’s domains . With this in mind, it was derived two
concepts of comprehension models, the top-down comprehension model and the bottom-up
comprehension models. The top-down approach is used when the programmer is familiar
with the program he’s trying to understand. He starts by creating a general hypotheses
about the program goal and how it is achieved by using his previous knowledge about the
program. This is called the expectation-based comprehension, where the programmer has
pre-generated expectations of the code’s meaning . After creating his hypotheses, the
developer starts searching in the code for algorithms/structures that he can link to his theory
and refines his hypotheses as he does that . This is the inference-based comprehension .
If the programmer has no previous knowledge about a program, he has to analyze line by
line to create a hypothesis about the program purpose. In this method, the developer starts
aggregating functions by their goals. What usually happens is that developers end by using
an integrated model  where they use top-down approach when possible and bottom-up
Although the way of coding varies from person to person, follow a guide of good practices
in programming (i.e. appropriate variable names and indentation rules ) can lead to an
uniformed structure that makes program comprehension easier. A different form of coding can
really slow down the comprehension of a program for the most experienced programmer .
D. Soares, M. J. V. Pereira, and P. R. Henriques 6:3
Nowadays, there’s even more factors that can affect program comprehension. For example, a
simple color schema, available in syntax-oriented text editor/IDE, helps the programmers
comprehending a program .
Newer studies have their focus not only on source code comprehension, but also on the
comprehension of structures, hierarchies and architecture of the program as well on the
relations between components .
Software visualization is the process of giving a visual representation of a program. It
takes the information that is presented in the source code and converts it to a graphical
representation with the goal of improving the comprehension of that program. Although a
visual image can help to understand a program, it’s not that simple since a basic program can
have multiple representations. The best one will depend on the task to be performed and what
the developer wants to know. This also means that each representation has to be thought
for specific tasks and a good portray of the system is imperative for it to be helpful. Simply
repackaging massive textual information into a massive graphical representation is not helpful,
so a linear translation is not ideal since experts want to see software visualised in context –
not just what the code does, but what it means . The visual representation of a program
can be focused in some aspects of the program and it can have different levels of abstraction.
This can also solve scalability problems because the visualization of big programs at once it
will not be a good help. So, the visualization tool must allow an high level of interactivity
to go one step at a time, filter the information, to establish the data that is wanted, to
define the level of abstraction and zoom facilities . Graphically these representations can
be based on diagrams, maps, icons, graphs and specific drawings. Some examples are the
Nassi-Shneiderman diagram used to represent a control-flow of a program ; Space-filling
(like tree maps or sunburts) allows to visualize in a very compressed way code metrics and
statistics ; Graphs are the most common representation and consists of nodes and arcs
where the nodes represent blocks of code and the arcs its flow .
In terms of functionality, there are two types of visualizations: static and dynamic .
A static representation can be seen as a simple image of the program, it doesn’t change over
time and it’s based on static information. Dynamic representations shows the information
that changes as the program is executed. A good example of an animated visualization tool is
Python Tutor. Python Tutor is an animation web tool that intends to help new programmers
to understand programs and currently supports some of the most popular languages at the
An interactive step by step presentation that shows what is really happening behind
every line of code during an execution instance (dynamic visualization) is the main feature
of Python Tutor. This allows the user to keep up with the modifications of values that each
variable suffers as each line of code is executed. The user has total control of this presentation
as he can choose if he wants to go to the next or previous step. Global variables are kept in
a global frame and then local variables just appear during the function execution.
Tools that produce static visualizations of source code are more usually found. AgileJ
StructuredViews  is a plugin for eclipse that generates UML class diagrams from the
source code. Since UML is a language used to structure software projects that is easy to
understand, it can be used to explain to the developers and even to the client. Moreover
it creates an easy to read and updated representation of the code. Sourcetrail  is a tool
that can be connected to an IDE or a text editor that displays an interactive dependency
graph and a search bar to search functions, classes or variables. Its interactivity allows the
user to see both an overview of the program and the dependencies between classes, methods
and variables. CodeSonar  offers a visualization software that shows an interactive call
6:4 Graph-Based Visualization Builder
Figure 1 Python Tutor visualization.
graph where it is possible to see the directories, files and functions rearranged hierarchically.
It allows the user to choose which metrics he wants to be displayed in the graph as well
compare functions/files based on that metrics.
3 Improving Python Tutor with graph-based visualizations
The main idea is to improve the platform Python Tutor by incorporating new features to it.
The objective is an interactive tool where the user can select what kind of graph he wants to
see in order of being able to analyze visually the flow of code or data dependencies in a part
of his code which execution is being animated. Three types of graphs are proposed:
Control Flow Graph (CFG): It is a representation of all paths (sequences of instructions)
that might be traversed during the execution of a given program; each node represents a
basic block of code (a sequence of instructions without alternatives, with just one input
and one output). By observing a control flow graph we can see to where the values go
and from where they came. It is a very useful graph to realize the dependencies that exist
in the program and which statements are influenced by others. For instance, a statement
B is control dependent on a statement A if B execution depends of the A outcome .
Data Flow Graph (DFG): In this graph there are two types of nodes where one represents
the values/variables and other the operators . This graph allows the user to observe
all operations that any variable suffers in its lifetime and in this way understand the
existing data dependencies. For instance, it is said that B is data dependent on A when
A value is used to compute B. This class of graphs is usually more difficult to understand
because they tend to represent a lot of information. Besides that, it is not so linear to
read as a CFG for example.
Function Call Graph (FCG): This class of graphs displays the relationships between a
function and the functions it calls . On one hand, FCG is useful to understand the
functions necessary to execute a function, and this is crucial to local errors and change
points. On the other hand, it allows the user to identify the most used functions and
D. Soares, M. J. V. Pereira, and P. R. Henriques 6:5
Figure 2 System Workflow.
the ones that are not being used at all. This information is extracted from the program
static analysis and knowing which functions are most called can be useful to improve
the program performance, since improving these functions will affect a big part of the
program. It is, usually, a very easy to read graph.
These new features were incorporated on Python Tutor adding a new field with a scroll
bar to select the graph type and a new button to generate the desired graph. Figure 2 shows
a diagram of the desired flow.
The source program to be visualized through the dependency graphs is inserted (typed, or
edited) at the home page of Python Tutor in order to reuse as much as possible the existing
functionalities. So the input text is sent to the back-end of Python Tutor to be compiled. If
the input program is correct and ready to be ran and animated, the front-end will receive the
execution trace. Once the input is validated, the system will open a new web page created
only to visualize the desired graphs. After the user choose the graph that he wants to see,
the input is passed through a new route to the graph generator. The graph generator will
create an image with the chosen graph and return it to the front-end. The front-end will
display the image on that new page so the user can visualize it and then remove the image
in order to avoid an overcrowding of files.
6:6 Graph-Based Visualization Builder
Figure 3 System architecture.
4 Integration Architecture and Developments
The Graph Builder was developed in Python using AnTLR to generate the parser and
static analyser for the source programs. It is incorporated into Python Tutor to increase its
functionality. The user uses Python Tutor editor to write the input program and then it will
be parsed by the syntactic analyser generated by AnTLR to identify the control and data
dependencies among program elements and represent them as graphs.
This analyser built by AnTLR from the Python grammar is able to recognize Python
programs. Once the information about the input program is gathered the next step is to
generate the graphs according to that information. Initially the graphs were built in Python
with the help of some libraries like ’igraph’. In a second phase, it was used PyGraphViz to
build better quality graphs with zoom features and more appealing for users.
Figure 3 shows a diagram depicting the architecture of the developed system.
As said above, to implement the referred parser it was required to define a Python
grammar. In this case, it was used the Python grammar available on AnTLR Github.
Depending on the information needed to extract, it was required to make some minor
modifications on the original grammar. For instance, splitting one grammar rule into three
different ones in order to treat the data more specifically. After defining the grammar,
semantic actions were specified in order to collect information and generate graphs. The
semantic actions were associated to each grammar rule and were constantly adjusted during
the development of the graph builder tool. Every time some piece of input code allowed to
detect an unhandled case, semantic actions were updated (added or changed) in one or more
grammar rules. The way the information was processed had to be as accurate as possible to
faithfully build graphs that deal with a large amount of data. So, the structures had to be
constantly updated with new information.
The three graphs presented use, by definition, a level of granularity where each node
corresponds to an instruction, which can be difficult to visualize for large programs. On the
other hand, Python Tutor itself has a limit of lines of code for the input program and even
so, for larger programs, it is possible to zoom in and zoom out of the graphs.
In the next section, the final structures chosen to represent each graph will be presented
and explained as well the information they hold.
4.1 Function Call Graph
There are two types of functions that can be called in a program: functions defined by the
developer or built-in functions (i.e., functions that are available from Python libraries). A
function is recognized by an identifier (its name) followed by parenthesis or, in case of being
a function written by the programmer, by the keyword def followed by its definition. All
the function calls found in a program are gathered in a Python dictionary where the key
D. Soares, M. J. V. Pereira, and P. R. Henriques 6:7
Figure 4 FCG construction.
is the name of the caller (the function that calls) and the value is the callee (the function
called). In Figure 4 it can be seen how the information required to create a Function Call
Graph is stored. On the final graph the two types of functions are distinguished by color:
blue for the functions created by the user, and green for Python built-in functions.
4.2 Control Flow Graph
The data for the construction of a CFG is stored in a similar way as the one explained for the
previous graph, that is represented in a dictionary with an entry for each defined function.
As the construction of this graph is not so linear as the previous one, it is required to save
more information in the dictionary. So, instead of saving just the code statement, it also
saves the type of that statement and the block number where it appears.
Analysing each statement, one can notice that some do not affect the execution flow
and others like loop (for or while) or conditional instructions (if,elif, else) have a
strong influence on the flow. Each node of the graph can have one or more edges coming
out depending on which type of statement it represents. For example, a node of loop or
conditional type will have two edges coming out of it, one in case of the condition being
true and the other in case of being false. A loop node will also have at least one more edge
coming in from the last statement inside the loop. An else node will always be preceded by
one node of a non-simple type.
The block number represents the statement level: the first level corresponds to the main,
other levels mean that is inside a (possibly nested) block depending on a condition. The body
(group of statements that depends on one statement) of non-simple statements will always
have a bigger depth level. When the depth level changes from one statement to another, it
allows the software to recognize that it will start a new block or that one just ended.
There are other statements that affect the flow of a program like for example the reserved
words break or continue. As can be observed in Figure 6, the CFG also contains coloured
arrows in order to be more intuitive for the user to identify which path is followed if the
condition present in the node is true (green arrow) or false (red arrow). Besides the color
difference, the true path arrowhead is filled unlike the false one that is not. These kind of
features can help the user to better read the graph.
6:8 Graph-Based Visualization Builder
Figure 5 CFG construction.
Figure 6 CFG Example.
D. Soares, M. J. V. Pereira, and P. R. Henriques 6:9
Figure 7 DFG construction.
4.3 Data Flow Graph
This sort of graph is not as linear as the previous ones because one variable depends not
only on the other variables or function results but also on loops or conditional statements.
There are also variables that only exist on a specific scope. Despite these constraints, the
information necessary to build DFG is gathered in the same way as the previous ones. In this
case, there are three types of tuples in the construction of this graph, one that represents a
change of value of a variable (variable name, block number, new value and operations that
produced the new value), one that serves to control the loops and conditionals dependencies
(depth level, statement, alternative path), and a third one to represent the case when a
variable can have multiple values depending on the execution path (all the possible variable
As can be seen in Figure 7, it is fundamental to use colors for the nodes in order to get
more intuitive drawings. Therefore it is used the color green for the nodes that represent
the variables which value changed, and in red the nodes that aggregate all possible values of
that variable at a given moment. In addition to use different colors different arrow types
are also generated as can be seen in figure 6. The other nodes depict values or operations.
This graph also includes boxes surrounding sub-graphs that represent loop blocks (cycles) or
conditional blocks aiming at producing clearer and more informative drawings.
5 Python Tutor with Graph Builder
To illustrate the project final outcome, Figure 8 shows a screenshot of Python Tutor integrated
with the Graph Builder. In this case it was generated a Control Flow Graph (showed in the
right side window) for the input program written in the left side window.
In order to assess the users’ satisfaction and the usability of the Graph Builder plugin
for Python Tutor, a specific experiment with Python Programmers as testers was designed
and conducted. For that purpose, it was created a survey that contained instructions for
the testers to follow in order to provide their opinion, answering some questions about
the new features. The testers should be able to write Python functions and analyse the
resulting graphs. This survey was completed by thirty one participants. The majority
of the participants were students or ex-students of a Master’s in Informatics Engineering,
thus fulfilling the necessary requirements. These participants already have programming
knowledge; that fact allows us to get the insights of people familiarized with the world of
6:10 Graph-Based Visualization Builder
Figure 8 Python Tutor with Graph Builder.
programming. The results so far obtained were very positive and increased the confidence
on this Python Tutor plugin (the Graph Builder); they also provided a good insight on its
relevance in the area of program comprehension. The survey also was helpful to identify
some minor bugs in the construction of the graphs; after being fixed, the accuracy and the
intuitiveness of the graphs were increased.
5.1 Survey Result Analysis
First, it will be presented the results related to the technical side, like the accuracy of the
tool and the complexity level of the functions used to test the feature. Then, it will be
discussed the opinion of the testers about the features offered by the new Python Tutor
plugin. These results, unlike the first ones, will be analysed separately for each graph so that
we can identify clearly those that can be more helpful for a deeper program comprehension.
5.1.1 Code Complexity
The first question of the survey was related to the complexity of the code the tester wrote.
In order to test a major diversity of programs each tester was free to chose the program to
use and this question was included to relate the correctness of the resulting graph with its
complexity. As it is observable on the pie-chart of Figure 9, the majority of the functions
written to test were of low complexity. This is not a problem for the desired assessment
because Python Tutor is directed to new programmers and, under these circumstances, the
expected inputs are of low to medium complexity. It is important to note that the complexity
of the program is subjective to whoever wrote it.
5.1.2 Graphs Correctness
The three graphics in Figure 10 allow to analyse the accuracy for each graph type.
D. Soares, M. J. V. Pereira, and P. R. Henriques 6:11
Figure 9 Complexity level of functions used to test the feature.
(a) FCG Accuracy. (b) CFG Accuracy. (c) DFG Accuracy.
Figure 10 Accuracy of each type of graph.
The numbers shown in Figure 10 indicate a good percentage of accuracy for each graph.
The most surprisingly result being the FCG (see 10a); it was expected to have the highest
accuracy but ended up being the one with the least. This may have happened because as
this is the simplest graph, the tests performed on this graph were not as intense as the others,
not allowing to catch as many exceptions like happened with the other graphs.
On the other hand, it was encouraging to realise how accurate was the CFG (see 10b),
and that the DFG got also a good rating (see 10c) mainly keeping in mind the amount of
dependencies necessary to show. These results were very satisfying mainly because the bugs
reported were corrected which means that this accuracy is now increased.
5.1.3 Graphs and Program Comprehension
In this subsection we will see whether graphs available can help people on comprehending a
program. The survey prepared for the experiment under discussion includes two questions
for each graph.
The first question aims at evaluating the intuitiveness of the respective graph, or in other
words, perceive whether the graphs are easy to read or not.
The second question aims at understanding if the tester thinks the graphs can help the
comprehension of the program being visualized.
6:12 Graph-Based Visualization Builder
According to the graphic in Figure 11b, 87,1% of the programmers who tested the tool
think that FCG can improve a program comprehension what follows the Figure 11a where
83.8% find it intuitive. This is because it is a very simple graph providing a generic overview
of the system behavior as it only shows the functions who are called by other functions.
(a) FCG Level of Intuitiveness. (b) FCG On Program Comprehension.
Figure 11 Perceptions on FCG.
The results shown in the pie graphics of Figure 12 reinforce the preconceived idea that
the CFG will be the strength of the new feature. These positive results boost the confidence
that this graph can really be helpful on program comprehension.
(a) CFG Level of Intuitiveness. (b) CFG On Program Comprehension.
Figure 12 Perceptions on CFG.
As expected, DFG was considered the least intuitive graph (see Figure 13a for details). As
shown in Figure 13b, although the lack of intuitiveness of this graph, a considerable amount
of people still thinks that it can help on the comprehension of a program, which means that
working out on the improvement of this component is a priority in the near future.
At the end, these results were very positive and increased the confidence on this Python
Tutor plugin (Graph Builder) and how it can aid on program comprehension. This survey
let us identify some minor bugs made along the construction of the graphs that after being
fixed increased their accuracy and intuitiveness.
D. Soares, M. J. V. Pereira, and P. R. Henriques 6:13
(a) DFG Level of Intuitiveness. (b) DFG On Program Comprehension.
Figure 13 Perceptions on DFG.
Control Flow Graph (CFG), Data Flow Graph (DFG) and Function Call Graph (FCG) are
examples of formal instruments that help to visualize statically some program perspectives
that depict the program behavior. Those instruments are considered important for professional
programmers to better comprehend the software systems they have to deal with. Taking this
statement as proved in area of Program Comprehension, we argued in this paper that they
can also be valuable artifacts to help beginner programmers to learn that topic. Moreover, if
we can combine the visualization of the referred graphs with program animation tools, we
believe that we can motivate and engaged students.
In this context, the paper reported a project aiming at building a tool that transforms
a function into its corresponding control and data flow graphs and to integrate it with the
program animator tool Python Tutor. At the end, it was proven that the usage of program
animation features complemented with dependency graphs visualization tools is feasible and
facilitates the comprehension of programs.
The ambition was to make this tool a general one that could help all computer program-
mers, not only beginners having to learn problem solving and deep their knowledge on a
specific programming language, but also professionals carrying out their software maintenance
tasks. Even recognizing that scaling up this feature to cope with large, real size, programs is
not so effective – because the corresponding graphs are big and confusing, and they require
more resources than those provided Python Tutor – we think that the experience succeeded
as a support to programming courses since it is covered the statements common to the most
used imperative languages.
The process of building this tool encountered obviously some obstacles. The biggest
difficulty faced was related to the Python Grammar used that does not cover all the real
program situations causing many compiling exceptions that frequently arose. Other challenge
was the integration with Python Tutor as this platform resorted to some outdated libraries.
However, all these troubles have been overcome ending up with a functional tool to help
understanding the algorithm and data structures implemented by small programs that
students have to study following an easy, attractive and fast learning process.
The design and conduction of new and more complete experiments involving a larger
number of students with different ages and in different programming courses, is the most
urgent task that needs to be performed to complete the project. It is important to show that
it is an effective way to help programming students. The developed tool (Graph-Builder) is
available at https://graph-builder.di.uminho.pt/visualize.html
6:14 Graph-Based Visualization Builder
As future work, the first proposal is to implement the same control-flow and data-flow
graphs for the other languages presented in the Python Tutor like Java and C. It is expected
to be easier to implement it now as the new languages will only need the construction of a
grammar for each language as the graph generator can be reused and the integration with
Python Tutor would only need a few adaptations. The other direction for the future of this
Python Tutor add-on is to improve the aspect of the DFG as the testers inquired said that it
is not very intuitive and easy to comprehend.
1 Agilej structureviews. http://www.agilej.com/.
2 Codesonar. https://www.grammatech.com/products/source-code-analysis.
3 Sourcetrail. https://www.sourcetrail.com/.
4 Luís Alves, Dušan Gajic, Pedro Rangel Henriques, Vladimir Ivancevic, Vladimir Ivkovic, Mak-
sim Lalic, Ivan Lukovic, Maria João Varanda Pereira, Srdan Popov, and Paula Correia Tavares.
C Tutor usage in relation to student achievement and progress: A study of introductory pro-
gramming courses in Portugal and Serbia. Computer Applications in Engineering Education,
n/a(n/a), 2020. doi:10.1002/cae.22278.
5 Marla Baker and Stephen Eick. Space-filling software visualization. Journal of Visual Languages
& Computing, 6:119–133, June 1995. doi:10.1006/jvlc.1995.1007.
6 Ruven Brooks. Towards a theory of the comprehension of computer programs. International
Journal of Man-Machine Studies, 18(6):543–554, 1983. doi:10.1016/S0020-7373(83)80031-5.
7 Luis M. Gómez-Henríquez. Software visualization: An overview, 2001.
8 Martin Hadley. 3 benefits of interactive visualization, January 2018.
9 Ben Holland. Call graph construction algorithms explained, March 2016.
10 François Lemieux and Martin Salois. Visualization techniques for program comprehension - a
literature review. In SoMeT, 2006.
11 A. Mayrhauser and A. Marie Vans. From program comprehension to tool requirements for an
industrial environment. In  IEEE Second Workshop on Program Comprehension, pages
78–86, August 1993. doi:10.1109/WPC.1993.263903.
12 Ike Nassi and B. Shneiderman. Flowchart techniques for structured programming. ACM
SIGPLAN Notices, 8:12–26, August 1973. doi:10.1145/953349.953350.
13 Michael O’Brien, Jim Buckley, and Teresa Shaft. Expectation-based, inference-based, and
bottom-up software comprehension. Journal of Software Maintenance, 16:427–447, November
14 Marian Petre. Mental imagery, visualisation tools and team work. In Proceedings of the Second
Program Visualisation Workshop, pages 3–14, January 2002.
15 Ben Putano. A look at 5 of the most popular programming languages of 2019. https:
//stackify.com/popular-programming-languages-2018/. Accessed: 26-9-2019.
16 Gerard Rambally. The influence of color on program readability and comprehensibility. ACM
Sigcse Bulletin, 18(1):173–181, February 1986. doi:10.1145/5600.5702.
17 Teresa M. Shaft and Iris Vessey. The relevance of application domain knowledge: Characterizing
the computer program comprehension process. J. Manage. Inf. Syst., 15(1):51–78, 1998.
18 Ben Shneiderman, Richard Mayer, Don McKay, and Peter Heller. Experimental investigations
of the utility of detailed flowcharts in programming. Commun. ACM, 20:373–381, June 1977.
19 J. Siegmund. Program comprehension: Past, present, and future. In 2016 IEEE 23rd
International Conference on Software Analysis, Evolution, and Reengineering (SANER),
volume 5, pages 13–20, March 2016. doi:10.1109/SANER.2016.35.
20 E. Soloway and K. Ehrlich. Empirical studies of programming knowledge. IEEE Transactions on
Software Engineering, SE-10(5):595–609, September 1984. doi:10.1109/TSE.1984.5010283.
D. Soares, M. J. V. Pereira, and P. R. Henriques 6:15
21 Marilyn Wolf. Computers As Components, Third Edition: Principles of Embedded Computing
System Design. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 3rd edition,
22 Mami Yoshida. Think-aloud protocols and type of reading task: The issue of reactivity in
l2 reading research. In Selected Proceedings of the 2007 Second Language Research Forum,
23 Andreas Zeller. Chapter 7 - deducing errors. In Andreas Zeller, editor, Why Programs Fail
(Second Edition), pages 147–173. Morgan Kaufmann, Boston, second edition edition, 2009.
Maria João Varanda Pereira
Pedro Rangel Henriques