Licy: A Chatbot Assistant to Better Understand and Select Open Source

Software Licenses

Giorgos Shittas, Georgia M. Kapitsaki

and Maria Papoutsoglou

Department of Computer Science, University of Cyprus, Aglantzia, Cyprus

Keywords:

Open Source Software Licenses, Chatbot, License Recommendations.

Abstract:

Open Source Software (OSS) carries licenses that specify the terms under which the software is made available

for use. Various resources are available for software engineers online, in order to assist them in understand-

ing and choosing among the available OSS licenses when creating their software projects. However, these

resources lack in the provision of a sense of interactivity to user prompts, which would have been useful for

providing guidance in a more familiar manner. In this work, we present our approach for Licy, a chatbot OSS

licensing assistant for guiding users with information on speciﬁc OSS licenses and for choosing which OSS

licenses to use in speciﬁc cases. A large number of licenses are supported by the chatbot using the license

model offered by choosealicense, focusing on license permissions, limitations and conditions. We describe the

design and implementation process of the chatbot and its preliminary evaluation results using chatbot design

metrics and a user evaluation. We argue that the chatbot can serve as a starting point for similar interactive

assistants for software engineers, and describe its value in that respect.

1 INTRODUCTION

Open Source Software (OSS) is widely used, while

many software systems are made available as OSS,

even by large vendors. OSS licenses specify the

terms under which the original software is available

for use by the licensees, the users of the software, and

how it can be further modiﬁed and distributed. The

OSS licensing landscape is complex, as it contains a

wide variety of licenses that are linked with differ-

ent combinations of permissions, constraints, and re-

quirements (Laurent, 2004). A very large number of

OSS licenses exists nowadays, with Software Package

Data Exchange (SPDX)

listing 634 OSS and con-

tent licenses including their different versions (Stew-

art et al., 2010). Even among the more limited number

of popular licenses, understanding the license content

and the context of use is not trivial. The OSS li-

censes that are the most dominant today, according to

Statista,

are the GNU General Public License (GPL),

the Apache License, and the MIT License.].

Although there are available resources for OSS li-

https://orcid.org/0000-0003-3742-7123

https://orcid.org/0000-0003-0658-5065

https://spdx.org/licenses/

https://www.statista.com/statistics/1245643/

worldwide-leading-open-source-licenses/

censes online, e.g. TLDRLegal

and choosealicense

and prior research works that have focused on license

recommendations and guidance (Kapitsaki and Char-

alambous, 2019; Kapitsaki et al., 2022; Xu et al.,

2023), there is a lack of tools that are interactive when

it comes to understanding and selecting OSS licenses.

In this work, we use as starting point the above to

explore the potential of chatbots as a solution to the

issue of OSS licensing assistants. Through simulat-

ing human-like conversations, chatbots deliver instant

responses, and automate functions that once required

a human touch, improving the user experience (Lok-

man and Ameedeen, 2019). We are introducing the

design and implementation of an OSS licensing assis-

tant chatbot, Licy, that aims at simplifying concepts

found in OSS licenses and at giving users a conversa-

tional environment for ﬁnding the license that is most

appropriate for their software project. The chatbot

supports 58 licenses, and its prototype implementa-

tion has been evaluated using chatbot design metrics

from a prior publication (Ca

nizares et al., 2022) and

via the participation of 18 users with limited experi-

ence in OSS. The results of this evaluation show that

Licy is straightforward to use, while it has the poten-

https://www.tldrlegal.com/

https://choosealicense.com/

Shittas, G., Kapitsaki, G. M. and Papoutsoglou, M.

Licy: A Chatbot Assistant to Better Understand and Select Open Source Software Licenses.

DOI: 10.5220/0013336400003928

In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2025), pages 573-581

ISBN: 978-989-758-742-9; ISSN: 2184-4895

573

tial to be expanded with additional features.

We argue that this work can serve as a starting

point for similar conversational agents in the area of

OSS licensing, especially for educational pusposes.

This interactive approach allows the chatbot to ask

speciﬁc questions, clarify users’ intentions, and give

recommendations, with which the user can interact in

order to get more information on speciﬁc responses

provided by Licy.

The remainder of the text is structured as follows.

Section 2 presents OSS licenses and related work in

the area. Section 3 is dedicated to the presentation of

the chatbot design and implementation. The evalua-

tion performed is detailed in section 4. Findings are

further discussed in section 5, and section 6 concludes

the work outlining directions of future work.

2 BACKGROUND AND RELATED

WORK

2.1 Open Source Software Licenses

The essence of software licensing is to protect the in-

tellectual property rights of developers, as well as to

specify the rights and restrictions given to licensees.

Software licenses range in general from strict propri-

etary to permissive OSS licenses, each tailored to in-

dividual goals. When using software that is made

available under an OSS license, the user has the

opportunity to add enhancements and distribute the

modiﬁcations made, usually under the same licens-

ing conditions as the original software. Understand-

ing and being compliant with OSS licenses is vital

in respecting the rights of software creators. It also

makes users of software conscious of any limitations

the software they are using has, like prohibitions to

use derivative works for commercial purposes or con-

ditions to share modiﬁcations under the same license.

Unlawful use of software resulting from the lack of

indicating any license or from the violation of licens-

ing terms can cause legal disputes, so proper selection

and use of OSS licenses is vital (Reddy, 2009). OSS

licenses are divided in the following main categories:

• Permissive Licenses: Licenses in this category

do not impose tight restrictions on how software

can be used, modiﬁed or distributed. Usually,

they require that certain conditions are met when

the software is redistributed, such as inclusion of

the original license notices, copyrights and trade-

marks. Examples of permissive licenses include

the Apache license, the BSD (Berkeley Source

Distribution) licenses and the MIT License.

• Weak Copyleft Licenses: Licenses in this cate-

gory allow users to choose any licensing model

for their own (added) code provided that the orig-

inal software is not modiﬁed. LGPL (Lesser

General Public License) licenses and MPL-2.0

(Mozilla Public License 2.0) are the most typical

examples of this category.

• Strong Copyleft Licenses: They guarantee that

any distributed version or modiﬁcation of the li-

censed original software, or any project that uses

it, will be made available under the same licens-

ing terms. This means that any software product

that employs source code licensed under a strong

copyleft license also needs to carry a strong copy-

left license, with examples including GPL and

AGPL (GNU Affero General Public License).

2.2 OSS Licensing Assistants

Prior works have focused on assisting users in choos-

ing the most appropriate license for their software.

ﬁndOSSLicense is a recommender system that assists

developers in selecting an appropriate license for their

software project (Kapitsaki and Charalambous, 2019;

Kapitsaki et al., 2022). The users can indicate a num-

ber of requirements, resulting from their responses to

speciﬁc questions, including rights they want to pro-

vide to users of the software, other licenses they are

already using in libraries in their source code, and

technical properties of the software, whereas licenses

recommended to other users of the recommender sys-

tem are also considered, when making recommenda-

tions. License compatibilities are also taken into con-

sideration using FOSSology for scanning the source

code (Gobeille, 2008). LicenseRec is a recommenda-

tion tool that is based on compatibility checks on OSS

projects’ code and dependencies (Xu et al., 2023).

It uses a compatibility matrix for compatibilities be-

tween licenses, whereas the source code is scanned

via the Scancode

tool to ﬁnd licenses in project ﬁles.

When it comes to license terms, OSLiFe-DiSC is a

tool that helps OSS developers in understanding open

source licenses terms (Sassi, 2024). OSLiFe-DiSC

provides users the list of OSS licenses approved by

the Open Source Initiative (OSI),

it allows users to

identify which licenses support a speciﬁc set of fea-

tures, while it can make comparisons between two li-

censes. At the same time, online resources provide

more information on the terms of licenses. The afore-

mentioned TLDRLegal is based on legal analysis of

licenses and presents what the licensee can, cannot

https://github.com/aboutcode-org/scancode-toolkit

https://opensource.org/

ENASE 2025 - 20th International Conference on Evaluation of Novel Approaches to Software Engineering

574

and must do with each license, while choosealicense

offered by GitHub provides a basic set of licenses the

user can choose from based on his/her needs and also

lists the main terms of a large number of licenses.

2.3 Chatbot Applications

Chatbots have been used in various areas, including

customer service, healthcare and education. In

customer service, chatbots are employed to respond

to queries, solve problems, and lead users through

troubleshooting, such as in the case of social media,

where a chatbot trained using Twitter conversations

between users and agents was created (Xu et al.,

2017). Within healthcare, chatbots help with ap-

pointments, schedules and patient education (Shinde

et al., 2021). In education, chatbots can act as virtual

tutors and learning assistants, providing students with

interactive learning environments (Clarizia et al.,

2018). Chatbots have also been useful for scientiﬁc

applications, such as for querying multidimensional

big data (Franciscatto et al., 2022), and for software

engineering (Lebeuf et al., 2017), whereas the

wide spread of Large Language Models (LLMs)

have offered new directions for integration with

chatbots (Dam et al., 2024). In the area of software

licenses, Microsoft License Advisor

is a custom

version of GPT-4 that assists users with advice on

Microsoft licenses.

Relation to Previous Works. Existing online re-

sources and platforms on OSS licensing offer a static

user experience, missing the interactive aspect, which

is available with a chatbot interface targeted in the

framework of the current work. The conversational

nature and variety of licenses supported in the cur-

rent work make the chatbot an enhancement upon

static approaches to OSS licensing assistance, with

Licy building upon the existing static resources, e.g.

the licensing terms of choosealicense have been used

for license modeling purposes.

3 DESIGN AND

IMPLEMENTATION

3.1 License Modeling

The chatbot supports the licenses listed in Table 1.

The abbreviation of the license name follows the nam-

ing convention of SPDX (Stewart et al., 2010) that

https://chatgpt.com/g/

g-p7NpdszNq-ms-license-advisor

has been utilized in prior works on OSS licensing and

are used this way in the text (Kapitsaki and Kramer,

2014). Although not intended for software licensing,

we have also added the following Creative Commons

licenses due to their popularity, as they are also li-

censes that gather the attention of developers and are

listed by SPDX: CC0-1.0, CC-BY-4.0, CC-BY-NC,

CC-BY-ND-4.0, CC-BY-SA-4.0, CC-NC-ND, CC-

NC-SA, CC-ND, CC-SA. For the same reasons, we

have also added the content licenses GFDL-1.3 (GNU

Free Documentation License v1.3) and OFL-1.1 (SIL

Open Font License 1.1) (Lin et al., 2009).

For each license, we adopted the license terms

available in choosealicense in order to be able to

present to the user what the licensee is allowed to do

(can or permissions), what he/she is not allowed to do

(cannot or limitations) and which are his/her obliga-

tions (must or conditions). These terms are also used

when recommending licenses to users. An example

for MPL-2.0 license as provided by choosealicense is

shown in Table 2.

3.2 Overall Chatbot Workﬂow

We have chosen to design Licy as a conversation-

centric chatbot that mimics human discussions, as

this type of chatbots are considered better in natu-

ral conversations (compared to other types of chat-

bots) (Moore and Arar, 2018). The functionality of

the chatbot concerning licensing has as goals to:

1. Simplify the license selection process for users by

recommending appropriate licenses.

2. Provide explanations on OSS licenses and answer

relevant users’ questions.

The workﬂow for the user was designed to cover

the two above areas, as depicted in Figure 1, that in-

cludes also the Rasa

process stack

(at the bottom part of

ﬁgure) that is the framework used for Licy’s implementa-

tion. The Rasa framework has been chosen due to its pop-

ularity and since it has an Open Source edition (Ca

nizares

et al., 2022). After the user provides his/her input, the in-

tention checker is used by the chatbot to analyze the user’s

input and decide on the actual intention. This is an impor-

tant stage where the chatbot determines what the users in-

tend to achieve with their query. When it comes to recom-

mending licenses, the chatbot is intended for beginner (Be-

ginner Questionnaire in the ﬁgure) or advanced users (Ad-

vanced Questionnaire in the ﬁgure), where beginners users

are shown a more limited set of questions. If license rec-

ommendation is detected as the user’s intent, these relevant

questions are shown to the user (the user indicates whether

he/she will be treated as a beginner or an advanced user).

https://rasa.com/

https://kavindasenarathne94.medium.com/

rasa-architecture-for-clever-chatbots-4a36b0b0ffcc

Licy: A Chatbot Assistant to Better Understand and Select Open Source Software Licenses

575

Table 1: OSS licenses covered by Licy.

License abbreviation and full name

1. 0BSD (BSD Zero Clause License)

2. AFL-3.0 (Academic Free License v3.0)

3. AGPL-3.0 (GNU Affero General Public License v3.0)

4. AML (Apple MIT License)

5. Apache-2.0 (Apache License 2.0)

6. APL-1.0 (Adaptive Public License 1.0)

7. APSL-2.0 (Apple Public Source License 2.0)

8. Artistic-2.0 (Artistic License 2.0)

9. BSD-2-Clause (BSD 2-Clause ”Simpliﬁed” License)

10. BSD-3-Clause (BSD 3-Clause ”New” or ”Revised” Li-

cense)

11. BSD-4-Clause (BSD 4-Clause ”Original” or ”Old” Li-

cense)

12. BSL-1.0 (Boost Software License 1.0)

13. CATOSL-1.1 (Computer Associates Trusted Open

Source License 1.1)

14. CDDL-1.0 (Common Development and Distribution

License 1.0)

15. CECILL-2.1 (CeCILL Free Software License Agree-

ment v2.1)

16. CPL-1.0 (Common Public License 1.0)

17. CUA-OPL-1.0 (CUA Ofﬁce Public License v1.0)

18. Cube (Cube License)

19. ECL-2.0 (Educational Community License v2.0)

20. EFL-1.0 (Eiffel Forum License v1.0)

21. EFL-2.0 (Eiffel Forum License v2.0)

22. EPL-1.0 (Eclipse Public License 1.0)

23. ErlPL-1.1 (Erlang Public License v1.1)

24. EUDatagrid (EU DataGrid Software License)

25. EUPL-1.1 (European Union Public License 1.1)

26. EUPL-1.2 (European Union Public License 1.2)

27. GPL-2.0 (GNU General Public License v2.0 only)

28. GPL-3.0 (GNU General Public License v3.0 only)

29. ISC (ISC License)

30. LGPL-2.1 (GNU Lesser General Public License v2.1

only)

31. LGPL-3.0 (GNU Lesser General Public License v3.0

only)

32. LPPL-1.3c (LaTeX Project Public License v1.3c)

33. MIT (MIT License)

34. MIT-0 (MIT No Attribution)

35. MPL-2.0 (Mozilla Public License 2.0)

36. MS-PL (Microsoft Public License)

37. MS-RL (Microsoft Reciprocal License)

38. MulanPSL-2.0 (Mulan Permissive Software License,

Version 2)

39. NCSA (University of Illinois/NCSA Open Source Li-

cense)

40. ODbL-1.0 (Open Data Commons Open Database Li-

cense v1.0)

41. OSL-3.0 (Open Software License 3.0)

42. PostgreSQL (PostgreSQL License)

43. Unlicense (The Unlicense)

44. UPL-1.0 (Universal Permissive License v1.0)

45. Vim (Vim License)

46. WTFPL (Do What The F*ck You Want To Public Li-

cense)

47. Zlib (zlib License)

The questions shown to beginner users have been adopted

from the main choices offered by choosealicense. We chose

this option in order to keep the available choices for begin-

ner users limited and simple.

Table 2: MPL-2.0 license modeling based on chooseali-

cense terms.

Key Value

title Mozilla Public License 2.0

spdx-id MPL-2.0

description Permissions of this weak copyleft license are

conditioned on making available source code

of licensed ﬁles and modiﬁcations of those

ﬁles under the same license (or in certain

cases, one of the GNU licenses). Copyright

and license notices must be preserved. Con-

tributors provide an express grant of patent

rights. However, a larger work using the li-

censed work may be distributed under differ-

ent terms and without source code for ﬁles

added in the larger work.

permissions commercial-use

modiﬁcations

distribution

patent-use

private-use

sublicense

conditions disclose-source

include-copyright

same-license

limitations liability

trademark-use

warranty

The questions shown to advanced users are more: 11

in total but based on the reply of the user to a given ques-

tion, some questions might be skipped. In order to deﬁne

these questions, we relied on prior works on recommend-

ing licenses, that we adapted considering the expanded list

of licenses covered in the current work in relevance to ﬁn-

dOSSLicense (Kapitsaki and Charalambous, 2016; Kapit-

saki and Charalambous, 2019). The questions added for

advanced users are listed in Table 3, whereas relevant ex-

planations are provided to the users for each of them (via

the chatbot’s User Interface - UI). The questions refer to

basic license terms, including whether modiﬁcations to the

software are allowed (question 1), whether the use of a dif-

ferent compatible license instead of the license of the origi-

nal software is allowed for derivative works (question 4 - for

instance, this is allowed under the MIT License but not un-

der GPL licenses), and whether interactions over a network

are covered (question 7 - covering mainly AGPL licenses).

Other requests, e.g. asking for information for speciﬁc

licenses, including permissions and restrictions or asking

for more general aspects of OSS licenses, are processed by

the chatbot. In order to answer a user’s request, the chat-

bot performs the next steps after the user types a message.

These are typical steps found in existing chatbot platforms,

such as Rasa or DialogFlow:

• Input Pre-Processing. The message of the user is pre-

processed, including text normalizing procedures, such

as converting to small letter cases, removal of punctua-

tion, as well as correction of mistakes in the message.

• Intent Recognition. Natural Language Understanding

(NLU) techniques are used, utilizing machine learning

https://cloud.google.com/dialogﬂow

ENASE 2025 - 20th International Conference on Evaluation of Novel Approaches to Software Engineering

576

Figure 1: General workﬂow of the Licy license assistant chatbot.

Table 3: Chatbot questions for license recommendations for

advanced users.

1. Do you want the license to allow modiﬁcations to your

software?

2. Do you want to allow the licensed software and deriva-

tives to be used for commercial purposes?

3. Do you want to allow your software to be used in propri-

etary (closed-source) applications?

4. Do you want to allow sublicensing of your software, e.g.

addition of a different license?

5. Do you want modiﬁcations to your software to be re-

leased under the same license (when distributing the li-

censed software)?

6. Do you want to specify that any modiﬁcations made to

your software must be indicated when redistributed?

7. Do you want to allow users that interact with the licensed

software via a network to receive a copy of the source

code?

8. Are you concerned about copyright, patents, or trade-

marks in your work?

9. Do you want the original copyright to be retained?

10. Do you want to give users the right to practice patent

claims of contributors to the code?

11. Do you want to allow the use of trademarks in your

work?

models that have been trained to analyze the text and

classify the user’s intent.

• Entity Extraction. Entities are extracted from the user’s

message. Entities are important details and names men-

tioned in the user’s message, like names of software li-

censes, license category, or other relevant information.

• Dialogue Management. The dialogue manager deter-

mines which response should be provided to handle

user’s input by considering the context of the conver-

sation and the training data of the chatbot.

• Action Selection. An action to be performed next is cho-

sen in this step. Actions can be to return a response to

the user, to ask the knowledge base or to invoke a cus-

tom action. More details on the chatbot’s knowledge

base that drives this step are provided in the next sec-

tion.

• Response Generation. A reply is formed using tem-

plates or responses deﬁned in the training data. The re-

sponse is customized in line with the user’s request and

utilizes any information which was found in the user’s

message.

• Follow-Up Actions. It is determined whether any ad-

ditional actions are needed after performing an action.

This is done by asking the user to provide more details

or revising the user’s query. For instance, the chatbot

may ask the user to verify the license name he/she is re-

ferring to (to ensure it was correctly recognized by Licy

as users may use slightly modiﬁed terminology to refer

to a speciﬁc license).

3.3 Chatbot’s Training Data and

Knowledge Base

In order to assist the chatbot in performing the steps de-

scribed previously, relevant data have been created. We

have created initially, training data that can be used to

understand user’s intent (Intent Recognition step in sec-

tion 3.2). The data have been created taking into considera-

Licy: A Chatbot Assistant to Better Understand and Select Open Source Software Licenses

577

tion the type of questions that can be asked for each license,

using as basis the license modeling (assuming that users will

focus mainly on these aspects of the license text), and the

variations in the license names that may be employed.

The above are reﬂected in the knowledge base of Licy,

whose main goal is to cover a wide range of user queries

in the area of license information and license recommen-

dations. Via the knowledge base, the chatbot is able to

understand the intention of user’s inputs. Most chatbots

are designed around intents. The license modeling has

been used in order to create the possible intents, combin-

ing different license names (and variations) with what the

license terms indicate. Some examples of potential in-

tents of the user when asking about licenses are provided

next. Each case corresponds to a speciﬁc user’s intent,

with the text in square brackets ([]) corresponding to the

entity’s text (parameter from the user’s text) and the text

in parenthesis (()) corresponding to the entity’s name, e.g.

[MIT](license name). The ﬁrst example provided concerns

variants of user’s intent to ask for information about a spe-

ciﬁc license, while the second variants of asking about a

speciﬁc permission a speciﬁc license may offer. The com-

plete list of options is available in the software repository

of Licy (Licy, 2024). The scope of what the chatbot can

understand and how it should respond to different user in-

puts is captured in a separate part of the conﬁguration, so

the intents and entities shown below are indicated in this

conﬁguration (domain.yml in Rasa framework):

− i n t e n t : a s k f o r l i c e n s e

e xamp l e s : |

−What do you know a b o u t [ AGPL− 3 . 0 ] ( l i c e n s e n a m e )

−G iv e me some i n f o on [ Vim ] ( l i c e n s e n a m e )

−G iv e me some i n f o on [CC−SA ] ( l i c e n s e n a m e )

−G iv e me some i n f o on [ EU D a t ag r i d ] ( l i c e n s e n a m e )

− I w an t t o know more a b o u t [ U n l i c e n s e ] ( l i c e n s e n a m e )

− I w an t t o know more a b o u t [ EPL − 1 . 0 ] ( l i c e n s e n a m e )

− I w an t t o know more a b o u t [ LPPL − 1 . 3 c ] ( l i c e n s e n a m e )

−What i s [CC−BY− 4 . 0 ] ( l i c e n s e n a m e )

−Have you h e a r d a b o u t [ EUPL − 1 . 1 ] ( l i c e n s e n a m e )

−[ A r t i s t i c − 2 . 0 ] ( l i c e n s e n a m e )

−[BSD−3− C l a u s e ] ( l i c e n s e n a m e )

[ . . . ]

− i n t e n t : a s k f o r l i c e n s e p e r m i s s i o n

e xamp l e s : |

−Does [ CPL − 1 . 0 ] ( l i c e n s e n a m e ) [ a l l o w ] ( c h o i c e )

[ p r i v a t e − u s e ] ( p e r m i s s i o n ) ?

− I s [ s u b l i c e n s i n g ] ( p e r m i s s i o n ) [ a l l o w e d ] ( c h o i c e )

f o r t h e [ MIT ] ( l i c e n s e n a m e ) ?

−Does [ GPL − 2 . 0 ] ( l i c e n s e n a m e ) [ p e r m i t ] ( c h o i c e )

[ m o d i f i c a t i o n s ] ( p e r m i s s i o n ) t o t h e s o u r c e co d e ?

−Does [ APSL − 2 . 0 ] ( l i c e n s e n a m e ) [ l e t ] ( c h o i c e ) t h e

l i c e n s e d m a t e r i a l an d d e r i v a t i v e s t o b e u s e d f o r

[ c o m m e r c i a l p u r p o s e s ] ( p e r m i s s i o n ) ?

−[ Can ] ( c h o i c e ) I [ mo d ify ] ( p e r m i s s i o n ) t h e s o u r c e co d e

o f [ CC−BY−NC ] ( l i c e n s e n a m e )

−[ Can ] ( c h o i c e ) I u s e [ c o m m e r c i a l l y ] ( p e r m i s s i o n )

t h e s o f t w a r e w i t h t h e [ E c l i p s e P u b l i c L i c e n s e 1 . 0 ]

( l i c e n s e n a m e ) ?

−Does [ CC−BY−NC ] ( l i c e n s e n a m e ) [ f o r b i d ] ( c h o i c e )

[ s u b l i c e n s e ] ( p e r m i s s i o n ) ?

−[EUPL − 1 . 2 ] ( l i c e n s e n a m e ) [ den y ] ( c h o i c e )

[ co m m er c i a l − u s e ] ( p e r m i s s i o n ) ?

[ . . . ]

In addition to intents, ﬂows are used to deﬁne exam-

ple conversations that a user might have with the chatbot.

These ﬂows guide the chatbot on how to respond to dif-

ferent user inputs based on the sequence of intents and ac-

tions. Essentially, they serve as training data for the chatbot,

helping it to learn how to manage dialogues by predicting

the next action based on the current state of the conversa-

tion (Dialogue Management and Action Selection steps in

section 3.2). Each ﬂow represents a potential path that a

conversation can take, capturing various user interactions

and the chatbot’s responses. By providing example con-

versations (or stories in the terminology of Rasa), the Rasa

framework trains its dialogue model to understand and pre-

dict the appropriate actions to take during real user inter-

actions. Each step in a ﬂow includes an intent that rep-

resents the user’s message or action. Some example sto-

ries (i.e. ﬂows) used in the chatbot are listed below (as for

intents, the whole list is available in the online repository

of Licy) (Licy, 2024). The action deﬁnes the chatbot’s re-

sponse to that intent, which can be a simple reply, a custom

action, or any other predeﬁned response. The names of the

intents indicated correspond to intents already deﬁned in the

knowledge base, as in the previous intent examples:

− s t o r y : Ask f o r s o f t w a r e l i c e n s e d e f i n i t i o n

s t e p s :

− i n t e n t : a s k f o r l i c e n s e d e f i n i t i o n

− a c t i o n : u t t e r l i c e n s e d e f i n i t i o n

− s t o r y : U s er a g r e e s on l i c e n s e

s t e p s :

− i n t e n t : a s k f o r l i c e n s e

− a c t i o n : a c t i o n g e t l i c e n s e

− i n t e n t : a f f i r m

− a c t i o n : a c t i o n g e t l i c e n s e i n f o

− s t o r y : U s er a s k s f o r l i c e n s e p e r m i s s i o n s

s t e p s :

− i n t e n t : a s k f o r l i c e n s e p e r m i s s i o n

− a c t i o n : a c t i o n c h e c k p e r m i s s i o n

3.4 Implementation Details

As aforementioned, the Rasa framework was employed for

implementation purposes, that uses Python, markdown and

YAML. The intents examples are used by the NLU al-

gorithms of Rasa, and are deﬁned in the respective ﬁle

(nlu.yml) with the training data that assist Rasa in identify-

ing users’ intents. Flows in the conversation are deﬁned in

the stories.yml ﬁle. The machine learning models present in

Rasa are trained using the knowledge base on how to com-

prehend human language. For implementation purposes, all

license terms (for the license modeling of section 3.1) were

encoded in JSON – JavaScript Object Notation – format. In

addition, the Firebase

cloud database was used for data

storage purposes, and Flask

Python web framework was

used for back-end routing. Standard web technologies were

employed for the front-end implementation, along with a

number of Python libraries used for NLU and Natural Lan-

guage Processing (NLP), e.g. PyPI.

For demonstration purposes, two screenshots from the

prototype of Licy are depicted in Figure 2 and Figure 3,

with the ﬁrst showing a case of asking information on a

speciﬁc license and the second triggering the questionnaire

on license recommendations. From the ﬁrst example,

the chatbot extracts two main parameters: the license

name (GPL2) and what the user is interested in (info).

The chatbot provides also the possibility to display more

information on the licenses, including relevant links (e.g.

TLDRLegal website).

Chatbot Availability. The chatbot source code and instruc-

tions for its installation are available in a software reposi-

tory online (Licy, 2024). The complete knowledge base of

https://ﬁrebase.google.com/

https://ﬂask.palletsprojects.com/en/3.0.x/

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578

Figure 2: User asking Licy for a speciﬁc license.

Figure 3: User asking Licy to recommend a license.

the chatbot as detailed in section 3.3 is also available in the

repository (Licy, 2024).

4 EVALUATION OF LICY

4.1 Metrics Calculation

As a ﬁrst evaluation step, we used the basic chatbot met-

rics introduced in a prior work (excluding the ones that re-

quire external tools), in order to get an overview of the gen-

eral design of Licy (Ca

nizares et al., 2022). For this pur-

pose, we have transformed the main conﬁguration ﬁles of

Rasa (conﬁg.yml, nlu.yml, stories.yml and domain.yml) to

the CONGA (ChatbOt modelliNg lanGuAge) chatbot neu-

tral notation (P

erez-Soler et al., 2020).

The results with the existing metrics are listed in Ta-

ble 4. Comparing the values with the metrics of the chatbot

dataset used in (Ca

nizares et al., 2022), it can be seen that

Licy covers a wide range of options: there are 14 differ-

ent entities (with the basic ones concerning license infor-

mation and recommendations), a large number of training

phrases and words per training phrase are used in intents

(only Dining-Out chatbot has a higher number of TPI than

Table 4: Basic metrics calculation for Licy (Ca

nizares et al.,

2022).

Name Description Value

INT # intents 14

ENT # user-deﬁned entities 0

FLOW # conversation entry points 12

PATH # different conversation ﬂow paths 12

Intent metrics

TPI # training phrases per intent 70.07

WPTP # words per training phrase 5.43

PPTP # parameters per training phrase 0.71

CPOP # characters per output phrase 121.60

Flow metrics

FACT # actions per ﬂow 1.33

FPATH # conversation ﬂow paths 1.00

CL conversation length 3

Licy and only googleChallenge a higher number of WPTP

among the 12 chatbot designs compared in (Ca

nizares et al.,

2022)), and the characters per output phrase are also high

(only FAQ-RASA-NLU in (Ca

nizares et al., 2022) has a

higher number). The conversation length (CL) is relatively

small, with the chatbot asking the user usually to conﬁrm

the license he/she is referring to, when asking for speciﬁc

license information (as in the examples in section 3.3).

4.2 User Evaluation Design

In order to evaluate the usefulness and the overall experi-

ence of Licy, a small scale user evaluation was performed.

Questions introduced focused on assessing the visual appeal

of the user interface, the user-friendliness of the chatbot,

and the overall ease of use. The questionnaire consists of

three main parts: 1) the ﬁrst part asks users about their ex-

isting knowledge on OSS and OSS licenses, 2) the second

part includes usability questions on the chatbot (e.g. how

easy it was to use it), 3) the ﬁnal part asks whether the chat-

bot was useful and their opinion on whether the informa-

tion provided is accurate. Users were also asked to indicate

any additional comments they might have, while access to a

chatbot deployment was provided so that users could inter-

act with the chatbot before answering the questionnaire.

In order to recruit participants, emails were sent to

Computer Science students and researchers within Univer-

sity of Cyprus. Users were informed that no personal data

would collected, and that any data used for research pur-

poses would not lead to the participant in any way. In order

to answer the questionnaire, participants gave their consent.

4.3 User Evaluation Results

18 individuals participated in the study, with all of them

being Computer Science students or researchers. Regard-

ing prior knowledge of OSS and OSS licenses (ﬁrst part of

questionnaire), most users have intermediate knowledge of

OSS and beginner knowledge of OSS licenses (Figures 4

and 5). Concerning usability aspects, participants found

Licy’s interface user friendly (61.1% found it very user

friendly and 33.3% user friendly). They also found buttons

and icons clear and easy to understand (94.4% of partici-

pants), with only one participant (5.6%) commenting that

“the close button for the about us screen was not clear.”

Licy: A Chatbot Assistant to Better Understand and Select Open Source Software Licenses

579

Figure 4: Participants knowledge on OSS.

Figure 5: Participants knowledge on OSS licenses.

The about us screen is part of the additional information

provided in the chatbot, as in the case of text with URLs

pointing to additional information on the OSS licenses sup-

ported. All users also indicated that it was easy or very

easy to use the chatbot and ﬁnd the information they were

looking for using Licy. Most users (88.9%) found that the

chatbot understood their questions clearly and two users in-

dicated that this happened only sometimes (11.1%).

On the use of the chatbot (third part of the question-

naire), not all users found it helpful and informative: 33.4%

did not (Figure 6). Nevertheless, most said they thought the

chatbot recommended the appropriate license(s) for them

(82.4% replied positively). Even though the number of par-

ticipants is small, we run statistical tests in order to examine

tendencies in the data that larger datasets should verify. We

run a Kruskal-Wallis H rank-based non-parametric test, us-

ing 1) whether the users found the chatbot helpful and infor-

mative and 2) whether they found the chatbot recommended

the appropriate license as the dependent variables, and the

participants prior knowledge on a) OSS and b) OSS licenses

as independent variables (Vargha and Delaney, 1998). No

statistically signiﬁcant results were found, indicating that

the prior knowledge of users on OSS and OSS licenses does

not affect their experience with Licy. The same conclusion

was drawn using as dependent variable 1) whether it was

easy to use the chatbot, and 2) whether the user can easily

ﬁnd the information he/she is looking for.

In the participants’ additional comments, where they

were also asked to indicated additional features they would

ﬁnd useful, some useful additional features emerged, in-

cluding the following: “Provide licenses that ﬁt on a more

general idea. Like I am creating an application for logistics

company what licenses should I check/need.”, “Information

such as in which cases it is useful to use a speciﬁc license

or real-world problems that require this license would be

helpful.”, “Be capable to understand natural language bet-

ter.”, “I would ﬁnd it useful if the chatbot responded to the

question “What can I ask you and how”.” The last sugges-

Figure 6: Participants’ opinion on whether chatbot is help-

ful and informative.

tion is already available in Licy, but the user might not have

used a relevant phrase the chatbot can understand to answer

this speciﬁc question (for training purposes, 18 alternatives

were provided to Licy).

5 DISCUSSION

For the chatbot implementation, a large number of licenses

was taken into consideration (58 licenses including content

licenses). Thus, the chatbot has a good coverage on

licenses users might be interested in, and can guide

license recommendations among all supported licenses.

Concerning the users comments on additional features

and better understanding of the user’s intent, indeed the

chatbot focuses on speciﬁc aspects – license details and

license recommendations based on the employed license

model – so it is expected that the users would prefer to

have more options as the user evaluation showed. For

novice users, it is more preferable to provide examples

of licenses for speciﬁc cases, without potentially going

into the details of the license terms. Nevertheless, the

current implementation of Licy can be useful primarily for

educational purposes, and can act as a guide for future

and junior software engineers. Its integration in Computer

Science courses or educational material for junior software

engineers could be considered as a useful implication of the

current work, and we argue that it may be more appropriate

in this setting, considering that at the current stage we have

not performed an evaluation with software engineers from

the industry (Ait Baha et al., 2024). We also argue that the

chatbot can be used as a starting point for creating relevant

conversational assistants on OSS licensing.

Limitations. The chatbot’s knowledge base is wide but it

does not cover all Open Source Software licenses avail-

able today (as aforementioned, SPDX lists 634 OSS li-

censes). Moreover, the chatbot’s license recommendations

are generic and rely on the text of licenses and the speciﬁc

terms as captured in the modeling of choosealicense, so

they may not address all legal concerns a user may have.

As such, the chatbot cannot be used to provide legal ad-

vice but offers guidance and recommendations that can be

later veriﬁed via the help of a legal expert. User evalua-

tion was limited to the users sampled that included students

and researchers and does not include feedback and experi-

ences from a wider population. Participants were mainly

young individuals and thus, users with limited experience.

The participation of senior researchers or developers from

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580

the industry may have led to different conclusions.

6 CONCLUSIONS

In this work, we have introduced Licy, a chatbot assistant

for OSS licensing. We have described the design and imple-

mentation process of Licy that currently covers 58 OSS li-

censes. The chatbot has been evaluated with chatbot design

metrics and via a small user population, showing promising

results for the future. As future work, we intend to utilize

LLMs to handle cases, where the chatbot is not able to re-

ply appropriately or does not understand the text provided

by the user. They have not been integrated in the current

state, as we wanted to focus initially on providing dedicated

training data speciﬁc to OSS licenses and their modeling.

We also aim to evaluate the chatbot with developers from

the industry and expand the cases addressed to include ad-

ditional licenses and multi-licensing schemes.

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