Question’s Advisor

A Wizard Interface to Teach Novice Programmers

How to Post “Better” Questions in Stack Overflow

José Remígio

, Franck Aragão

, Cleyton Souza

1,2

, Evandro Costa

and Joseana Fechine

Federal Institute of Education, Science and Technology of Paraíba, IFPB, Monteiro, Paraíba, Brazil

Federal University of Campina Grande, UFCG, Campina Grande, Paraíba, Brazil

Laboratory of Artificial Intelligence, LIA, Campina Grande, Paraíba, Brazil

Keywords: Stack Overflow, Answerability, System, Question Quality.

Abstract: Programmers often recur for online communities in order to find help for a current problem that they are

facing. However, after sharing a question, its author has no guarantee if he will receive an answer, neither

when. Recent studies have found that low quality is one of the top reasons why questions remain

unanswered. In this work, we conducted a qualitative study aiming identifying what programmers are

looking in a question that they decide to answer. Based on this feedback, we designed a tool to help

programmers to write high quality questions. We named the app Questions’ Advisor, due his role of helping

but without forcing the user to follow it, and it is available for desktop and mobile clients. We believe it

could be very helpful, especially for novice programmers.

1 INTRODUCTION

Stack Overflow is a Community Question and

Answering (CQA) site for professional and

enthusiast programmers. It is built as part of the

Stack Exchange platform of Q&A sites and it is the

largest community of the network with 6 million

users and over 12 million asked questions

. Stack

Overflow works like a thread based community. The

questions’ titles are presented in a “wall”, ordered by

their latest interaction, and, by clicking them, users

can see the description and answer it.

In general, questions in Stack Overflow are

answered in a very short time (Mamykina Manoim

and Mittal, 2011). However, after sharing a question,

its author has no guarantee if he will receive an

answer, neither when. According Hao, Shu and

Irawan (2014), over the years, the number of

unanswered or ignored questions is constantly

increasing. Interestingly, the fact that those

questions are not answered is not caused by users

not having seen them (Baltadzhieva and Chrupała,

2015). One of the main reasons to a question being

http://stackoverflow.com/company/about

ignored is its low quality (Asaduzzaman et al.,

2013).

Some recent studies have found that there is a

correlation between the question characteristics and

its responsiveness. In Facebook, for example,

Teevan, Morris and Panovich (2011) found that a

concise style of question-asking, a defined scope (or

audience), and the inclusion of a question mark were

associated with more and higher quality responses

within shorter periods of time. Regarding Stack

Overflow, many works suggest that the quality of

the question itself can have an important effect on

the likelihood of getting useful answers

(Baltadzhieva and Chrupała, 2015).

In this work, we asked programmers which

characteristics they were expecting when they

choose a question to answer. Based on the feedback,

we designed this hybrid app (mobile and web) to

help novice programmers to post “better” questions

on Stack Overflow. The user “scratches” his

question in the app and receives suggestions on how

to improving the quality of the question. The

suggestions are the result of a Natural Language

Process (NLP) analysis over the question, which

aimed identifying the “good” characteristics that are

missing.

Remígio, J., Aragão, F., Souza, C., Costa, E. and Fechine, J.

Question’s Advisor - A Wizard Interface to Teach Novice Programmers How to Post “Better” Questions in Stack Overﬂow.

DOI: 10.5220/0006389504710478

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 1, pages 471-478

ISBN: 978-989-758-247-9

471

The survey application resulted in a list with

sixteen characteristics. Most respondents agreed that

only a little portion of Stack Overflow’s questions

have high quality. Previous studies have already

found that it is possible to teach people to ask better

questions (Sullins et al., 2015). Thus, this assistance

could be very useful to improving the general

question quality in the community.

This work is organized as follows. Section 2

presents related work and how what we are

proposing differs from them. Section 3 details the

results from the survey application, as well our list

of “good characteristics”. Section 4 explains how we

plan to change the usual Q&A process and Section 5

describes how the app works. In Section 6, we

discuss the conclusions and future work.

2 RELATED WORK

According Liu, Bian and Agichtein (2008), low

quality questions often lead to bad answers whereas

high quality questions usually receive good answers.

In (Souza et al., 2016a), it was presented a case

study that demonstrated the advantages of writing

high quality questions over poor quality questions,

in real world situation. In addition, Ravi et al.,

(2014) analysis showed that higher quality questions

continue to garner interest over time in comparison

to lower quality questions.

However, writing high quality question may not

be intuitive for all. In a CQA, a good question is not

just one that is found to be useful by other people: a

question is good if it is also presented clearly and

shows prior research (Ravi et al., 2014). Hao, Shu

and Irawan (2014) identified 14 features that will

help in the increasing of the quality of questions, in

Stack Overflow. In their work, the authors found

that the following content-related features are

positively associated with question quality: “w”

word, completeness, and subjectivity; while question

length, title length, number of tags, code snippet

presence, complexity and politeness are negatively

associated. Later, a study from (Baltadzhieva and

Chrupała, 2015), identified that questions containing

incorrect tags, or that are too localized, subjective or

off topic are considered of bad quality. On the other

hand, the presence of an example has a positive

effect on the question score and the number of

answers (Baltadzhieva and Chrupała, 2015).

Mamykina and Manoim, (2011) found that 92%

of Stack Overflow questions are answered in a

median time of 11 minutes. Their research suggests

that aspects of the question may influence the speed

of response, with, for example, questions that invite

discussion being less likely to receive fast responses.

According Treude, Barzilay and Storey (2011) the

most common use of Stack Overflow is for how-to

questions. The site is also effective for code reviews,

explaining conceptual issues and answering

newcomer questions (Treude, Barzilay and Storey,

2011). The type of question is not the only factor for

getting good answers. Other factors seem to include:

the technology in question, the identity of the user,

the time and day in which the question was asked,

whether the question included a code snippet, or the

length of the question (Treude, Barzilay and Storey,

2011). The thoughts about the influence of the

question length are mixed and contradictory and

further research is still necessary in order to provide

better insights in its importance for the number of

answers and the question score, according

(Asaduzzaman et al., 2013).

Asaduzzaman et al., (2013) proposed the

taxonomy to explain why questions remain

unanswered in Stack Overflow. The top five reasons

are: “Fails to attract an expert member”, “Too short,

unclear, vague or hard to follow”, “A duplicate

question”, “Impatient, irregular or inconsiderate

members” and “Too hard, too specific or too time

consuming”. Understanding those factors that

contribute to questions being answered as well as

questions remain ignored can help information

seekers to increase their chances of getting answers

from the community.

Dror, Maarek and Szpektor, (2013) proposed

using this information to give users immediate

feedback about the ability of his question in

attracting answers. Imagine that a user is preparing

to broadcast a question in a CQA. If he knew which

factors can affect response rate, he could shape his

request to fit these factors and theoretically improve

his chances of finding help. According Sullins et al.,

(2015) it is possible to teach people to ask better

questions. Their case study revealed that participants

in the question training condition asked significantly

more “deep” questions on the post-test than did the

participants in the control condition.

3 A NEW WAY TO ASK

QUESTIONS ONLINE

Figure 1 illustrates the traditional social query

process.

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Figure 1: The traditional social query process.

The process starts with the user accessing the

website. He, alone, phrases his problem and shares

with all users in the collaborative environment. In

Figure 7, we used a CQA as example, but it could be

any social context like a social network or e-mail’s

group, for instance. Some collaborative

environments could even provide recommendations

of which users are able to respond (query routing).

However, the literature review opens the

following research opportunity: assisting the user in

the task of including these “good” characteristics

into the question structure in order to enhance Q&A

experience (improving both question quality and

attractiveness). We could change the way how social

query works.

Basically, we could include a step, before the

question is released, in which we assist the user in

the task of formulating his problem through the

system interface. This assistance aims to help users

insert “good” characteristics into their questions.

Thus, they are, theoretically, improving their

chances of finding help. This new process is

presented in Figure 2.

The main difference between the two processes

is that, instead of writing the question alone, in this

new one, the user is somehow assisted by the

website interface. The user interface (UI) “knows”

which characteristics a question should have. It

analyzes the question and searches for the presence

and absence of these “good characteristics”.

The UI could give tips on what “good

characteristics” are missing in the question’s

structure or suggest rewritten versions of the original

question, but with the “good characteristics” already

implemented. For instance, if most questions that are

answered have a certain length, the analysis consists

in checking if the new question has this exact length

Figure 2: New social query process.

or is close to it. If it does not have it, adjusting the

question length will be one of the suggestions

outputted by the UI. User will receive this feedback

and decide if he wants to follow it or not. He also

decides in what extension applying the suggestions.

In addition, if we are in a context where query

routing works, during “Assistance Phase”, the user

could also be inquired about reducing the scope of

the Expert Search establishing demographic filters.

After “Assistance Phase”, the query routing would

proceed normally.

This “assistance phase” would be more efficient

to improve question quality and attractiveness than

using the “good characteristics” list as guidelines to

users. Imagine a context where there are fifty

desirable features that a question could have. It is

unlike that all users will study this entire list to ask

good questions. However, if the UI “hints” users

only with suggestions about features that are missing

in the question, probably it would be easier to him to

follow.

Including this “assistance phase”, however,

would demand previously identifying which are the

“good characteristics” that a question should have at

that context. It is important to highlight that these

“good characteristics” are strongly related to the

studied context. The list of “good” characteristics is,

in Figure 8, obtained through the investigation of

CQA’s questions history. However, this list of

characteristics could also be obtained through: (1)

interviews with active users asking them which

factors attract them to answer a question; and (2)

surveying the literature about question asking in that

environment to identify good practices and

characteristics that impact response rate. We

surveyed the literature to present a preliminary list

Question’s Advisor - A Wizard Interface to Teach Novice Programmers How to Post “Better” Questions in Stack Overﬂow

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of “good characteristics”, but, before designing our

solution, we also collected users’ opinion through a

questionnaire, in order to identify which are the

“good characteristics” that a question should have to

motivate them to answer.

In next section, we discuss these and others

outcomes of the survey application.

4 SURVEY APPLICATION

We elaborate a survey to ask users from

Programming CQA how close our first drawing of

“good characteristics” was from what they was

looking for in questions. In addition, we asked

which characteristics they were expecting that were

not included in the list yet. We collected 400

answers. Before summarizing the results of the

survey application, we want to start describing the

profile of people who answer the questionnaire.

4.1 Respondents’ Profile

Figure 3 describes the occupation of the

respondents.

Figure 3: Occupation of the Respondents.

Students represent the larger part of our sample

(46%). Since the questionnaire was broadcasted

through the university channels, this was expected.

The remaining 44% work with programming related

jobs (analyst, tester and, of course, programmer).

Only 10% of them work as a professor.

Most respondents access these sites in a weekly

(39%) or daily (36%) basis. In addition, we also

found that while most Programmers (62%) accesses

these sites in a daily basis, most Students (41%) are

weekly visitors. Probably, this is consequence of

their relation with Programming. While professional

Programmers deal with Programming daily, as part

of their profession, Students check the site

sporadically, since their relationship with

Programming is not so intensive, when they are

facing homework or just studying.

Figure 4 shows which activities respondents

perform when they visit these sites (multiple choices

were allowed).

Figure 4: Actions of the Respondents.

We found interesting that asking questions and

searching for questions similar to a current problem

were such common roles. People run to Stack

Overflow more often than the own documentation of

the technology that they are using, when they are

facing a problem. This just highlights the

collaborative aspect of programming. Unfortunately,

our sample is only composed 20% by people who

actually answer questions. However, this was

already expected, since the respondents are a small

portion of the entire community (Furtado et al.

2013).

4.2 Respondents’ Open Suggestions

Since we did not want to influence respondents with

our list, we started asking which characteristics they

believed was related with question attractiveness and

quality, in their own opinion. We used an open

question and we tabulated answers, in order to

identify the more frequent suggestions.

Table 1 shows the top five characteristics

(grouped by respondent occupation).

In Table 1, we can see that two characteristics

were related to both question attractiveness and

quality. They are: (1) objectivity, and (2) clarity.

These are all subjective characteristics. It also worth

mention that programmers like to answer questions

from people who know about the topic they are

asking, indicating that programmers do not like to

answer newbies’ questions. In addition,

programmers and professors associated the code or

example presence with question quality.

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Table 1: Number of mentions of characteristics related to

question quality and attractiveness.

Characteristic related with question attractiveness

Characteristic Prof. Stud. Prog.

Other Total

Example or code. 18 77 56 24 175

Objectivity. 17 74 38 35 164

Clarity. 12 57 16 20 105

Short description. 7 30 16 14 67

Coherence

between title and

description.

5 33 12 14 64

Characteristic related with question quality

Characteristic Prof. Stud. Prog.

Other Total

Clarity 9 61 22 21 113

Objectivity 10 47 26 17 100

Show that knows

the topic.

7 32 35 19 93

Example or code. 9 24 30 9 72

Be polite. 5 26 12 11 54

4.3 Respondents’ Agreement Rate

In (Souza et al., 2016a), we performed a literature

review that aimed to draft a first version of the

“good” characteristics list. In (Souza et al., 2016b),

we found interesting correlations between the

presence of these “good” characteristics and

questions’ performance.

We used the questionnaire to capture people’s

opinion about this version of our “good”

characteristics list. In the last part of the

questionnaire, we presented this first drawing and

we asked if the respondent agreed that the presence

of each characteristic was important. Table 2 shows

the percentage agreement of the respondents with

each “good” characteristic.

In Table II, we can see that two characteristics

have a high disagreement percentage: Title entirely

written in capital letters and prioritizing long

description. We believe that long description

requires more effort from the user to answer,

discouraging most people. In addition, the

disagreement related to the use of capital letters is

due its Internet meaning, which it is usually taken as

yelling. These characteristics were not considered

later on our study.

Table 2: Agreement rate with “suggested” characteristics.

Characteristic Yes No None

Well-chosen title 94% 1% 5%

Title partially written in capital

letters

10% 34% 56%

Title entirely written in capital

letters

2% 62% 36%

Coherence between question

description and title

97% 1% 2%

Understandable description 95% 1% 4%

Including a vocative 12% 30% 59%

Prioritizing short description 47% 18% 36%

Prioritizing medium size description 43% 6% 51%

Prioritizing long description 12% 55% 34%

Showing an example 82% 5% 13%

Avoiding a large amount of code 51% 22% 28%

Avoiding description with only code 60% 15% 25%

Restricting each question to a single

problem

72% 7% 21%

Including greetings 42% 10% 48%

Using proper language 44% 9% 47%

Avoiding creating duplicate

questions

81% 4% 16%

Avoiding creating factoid questions 27% 31% 42%

Do not create homework questions 76% 5% 19%

Including links related to the

question

65% 5% 30%

Combining links with partial

content

53% 7% 40%

The following characteristics received a high

“indifferent” rate: Title partially written in capital

letters, including vocative, prioritizing medium size

description, including greetings, using proper

language, avoiding creating factoid questions and

combining links with partial content. These

characteristics were not entirely considered on our

study, as we will explain next.

After analyzing the responses for both, the open

and the objective, questions, we reduce our list of

“good” characteristics to the following:

1) Objectivity: objectivity was the top

characteristic for both quality and attractiveness,

Question’s Advisor - A Wizard Interface to Teach Novice Programmers How to Post “Better” Questions in Stack Overﬂow

475

according respondents. However, it is a strongly

subjective concept to identify. Most dictionary

definitions emphasize the shortness aspect of

objectivity. Thus, we will reduce Objectivity to these

other characteristics: (1) Restricting each question to

a single problem; and (2) Prioritizing short

description. Although we summarized objectivity in

only these two features, we believe that the

objectivity’s presence is also related to other features

included on the list, such as “Clarity” and “Well-

written description”.

2) Clarity: clarity was another top mentioned

characteristic and, similarly to objectivity, it is a

strongly subjective concept too. We define clarity as

the quality of being easily understood. We decide for

checking clarity through these three other

characteristics: (3) Coherence between question

description and title; and (4) Making the problem

the more evident as possible in the description.

3) Well-written description: although users want

to help, poorly written questions (vague or

incomplete, for instance) will discourage them. For

this reason, it is important to make an effort in

writing self-contained questions. We believe that

these hints would help in this matter: (5) Including

example or code; (6) Including links related to the

question; (7) Combining links with partial content;

and (8) Avoiding description with only (or a large

amount of) code.

4) Be polite: – there were (a few) mentions, in

open suggestions, that the politeness of the asker

was one of the factors that courage people into

helping. Thus, we included in the final list these

etiquette rules for asking questions online: (9)

Avoiding creating duplicate questions; (10) Do not

create homework questions; (11) Including

greetings; and (12) Using proper language.

This list summarizes what the community wishes

all questions look like. Thus, based on this list, we

develop a suggestion engine, which analyzes

questions looking for the absence of each one of

these 16 characteristics and giving feedback to the

user on how to improve his question’s quality by

adding the ones that are missing. The NLP

techniques were programmed using CoGroo

(http://cogroo.sourceforge.net/), OpenNLP

(https://opennlp.apache.org/), and LanguageTool

(https://www.languagetool.org/).

Next, we will present the mobile app, which it is

an instance of this suggestion engine.

5 PRESENTING THE APP

We named the app Question’s Advisor, due his role

of helping but without forcing the user to follow it.

The software was developed using Progressive Web

Apps (PWA) technology, which makes it suitable

for Desktop and Mobile clients. One can access our

prototype using this address

http://appif.herokuapp.com/.

In Figure 5 (left), we show the login screen of

the Question’s Advisor (we will illustrate using the

mobile vision). When the user clicks in “authorize”,

he is requested to login using his Stack Overflow

credentials (center). This process is mandatory to

allow the app publishing questions in Stack

Overflow using the user’s account.

Figure 5: Login screen and authorize screen from

question’s advisor.

After login, we ask the user the mandatory

permissions for reading and writing using his

account, this also can be seen in Figure 5 (right).

After, the user is directed to the home screen of the

app, that it is presented in Figure 6.

Figure 6: Home screen from Questions’ Advisor.

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In Figure 6, we can see an empty list (left), but

the home screen is also able to list all questions

published by the user (right). Below each question

on the list, he has a summary of the question’s

performance regarding number of answers, points

and views. When clicking in one of questions, the

user is directed straight to Stack Overflow, where he

can check the activity over his question. The “plus”

button in the home screen (or the lateral menu)

directs the user to the new question screen, inside

the app. This screen is presented in Figure 7.

Figure 7: Writing a new question using Question’s

Advisor.

In Figure 7, we can see that the description of the

question may include code snippets and they will

appear with a different style to make the reading

easier. Moreover, the user can also add tags to

describe the question subject. These tags will be

published on Stack Overflow too. While making

usability tests, most users prefer writing their

question accessing the app through a computer

instead a smartphone, but both “visions” offer the

exact same functionalities.

After writing the question, the user will click on

“publish”. However, before the sharing the question,

he will receive a feedback from the app about how

he can improve the question; this can be seeing on

Figure 8 (left).

In Figure 8 (center), we can see the list of

suggestions too. The suggestions are the result of a

Natural Language Process (NLP) analysis over the

question, which aimed identifying the “good”

characteristics that are missing. The user can roll

down this list and he will see the “ignore” button

and the “review” button. If the user clicks in

“ignore”, the question will be immediately shared on

Stack Overflow. If the user clicks in “review”, he

will have the chance of following the suggestions

and rewriting the question.

Figure 8: Checking the Question and Suggesting

Improvements.

After editing the question, the user will click

again in “publish” and the Question’s Advisor will

calculate if the number of “good” characteristics has

increased in comparison with the last analysis

(indicating a gain in the question quality). In the

case where the number of “good characteristics” fell,

the user will keep receiving suggestions on how to

improve the question (he will be able to ignore

again, if he prefers).

After publishing, the user will receive a

confirmation that his question was published in the

Stack Overflow, as presented in Figure 8 too (right),

and the question will appear in the Home Screen.

6 CONCLUSIONS AND FUTURE

WORK

Programmers often recur for online communities in

order to find help for a current problem that they are

facing. However, CQAs like Stack Overflow, while

are efficient for this goal, does not ensure an answer

for all question. The number of ignored questions is

constantly increasing. One of the reasons why

questions remain unanswered is due their low

quality. In addition, some studies found a correlation

between the question’s characteristics and its ability

of drawing attention and being answered.

In this work, we conducted a mixed study aiming

identifying what programmers are looking in a

question that they decide to answer. We designed a

questionnaire where people could suggest their own

thoughts and also agree or disagree with our opinion.

Based on these answers, we developed a tool to help

programmers to write high quality questions. Our

solution analyses the original question written by the

user and suggests including the missing “good”

characteristics. We named the app Questions’

Advisor and it is available for desktop and mobile

Question’s Advisor - A Wizard Interface to Teach Novice Programmers How to Post “Better” Questions in Stack Overﬂow

477

clients. We believe it could be very helpful,

especially for novice programmers.

For future work, we aim to test our solution

locally, collecting feedback, and reporting what

happened. In addition, we need to do a further

investigation on how the presence of these features

relates to question’s performance. This research

would also have the goal of finding new relevant

characteristics that were not suggested though the

questionnaire. Last, we want to design a plugin that

allows people to add our suggestion engine in any

text area component. This way, our solution could

be added into small learning communities from

Moodle or Facebook, for instance.

ACKNOWLEDGEMENTS

We want to thank IFPB for all the support during the

execution of this research.

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