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Accepted for 27th IEEE International Requirements Engineering Conference, http://re19.ajou.ac.kr/.

Winkler, Jonas Paul; Grönberg, Jannis; Vogelsang, Andreas (2019): Optimizing for Recall in Automatic
Requirements Classification: An Empirical Study. 27th IEEE International Requirements Engineering
Conference (RE'19).
Jonas Paul Winkler, Jannis Grönberg, Andreas Vogelsang
Optimizing for Recall in Automatic
Requirements Classification: An Empirical
Study
Accepted manuscript (Postprint) Conference paper |

Optimizing for Recall in Automatic Requirements
Classification: An Empirical Study
Jonas Paul W inkler
T echnische Uni versität Berlin
Berlin, Germany
[email protected]
Jannis Grönber g
T echnische Uni versität Berlin
Berlin, Germany
jannis.r [email protected]
Andreas V ogelsang
T echnische Uni versität Berlin
Berlin, Germany
andreas.v [email protected]
Abstract —Using Machine Learning to solv e requirements en-
gineering problems can be a trick y task. Even though certain
algorithms ha ve exceptional perf ormance, their recall is usually
below 100%. One k ey aspect in the implementation of machine
learning tools is the balance between r ecall and precision.
T ools that do not find all correct answers may be consider ed
useless. Howe ver , some tasks are v ery complicated and even
requir ements engineers struggle to solve them perfectly . If a tool
achiev es performance comparable to a trained engineer while
reducing her w orkload considerably , it is consider ed to be useful.
One such task is the classification of specification content
elements into requir ements and non-requir ements. In this paper ,
we analyze this specific requir ements classification problem and
assess the importance of r ecall by perf orming an empirical study .
W e compared tw o groups of students who perf ormed this task
with and without tool support, respecti vely .
W e use the results to compute an estimate of β f or the F β scor e,
allowing us to choose the optimal balance between pr ecision and
recall. Furthermor e, we use the results to assess the practical
time sa vings realized by the appr oach.
By using the tool, users may not be able to find all defects
in a document, howe ver , they will be able to find close to all of
them in a fraction of the time necessary . This demonstrates the
practical usefulness of our approach and machine lear ning tools
in general.
Index T erms —Empirical resear ch, controlled experiment, ma-
chine learning, automation
I . I NTR ODUCTION
Recent adv ances in natural language processing and machine
learning led to an increasing number of approaches that try
to solve requirements engineering tasks by some form of
automation [
1
]. In almost all cases, the automatic approaches
are not able to solve the tasks perfectly , i.e., with 100%
precision and 100% recall. Therefore, most authors argue that
the approaches aim to support the requirements engineer in
performing a task. Ho wev er , it is not clear what quality a tool
must achie ve to justify this claim. An anecdotal e xample is
gi ven by Berry [
2
] who ar gues that for problems where all
correct answers ha ve to be found, e very tool with recall belo w
100% is useless because the requirements engineer needs to
inspect the entire document anyw ay to identify the (few) correct
answers that the tool missed. Even for cases not as e xtreme as
this, recall is usually considered more important for automating
requirements engineering tasks than precision [
2
]. Ho wev er ,
many authors still use the
F 1
score to optimize and e valuate
their approaches, which weighs precision and recall equally .
One specific task in the requirements engineering process
is the classification of specification content elements into
requirements and non-requirements. While requirements are
the basis for tests and define what is leg ally binding for
the contractor , non-requirements may contain e xplanatory
information, examples, as well as figures, tables and references
to other documents. W e hav e proposed an approach that auto-
matically classifies natural language sentences in requirements
specifications into requirements and non-requirements [
3
]. In
our paper , we used
F 1
to e valuate the performance of our
approach. In this paper , we instead follo w the suggestions of
Berry [
2
] and deri ve a reasonable v alue for
β
for the problem of
finding defects in requirements/non-requirements classifications.
W e used our tool to identify classification defects in already
labeled requirements specifications and performed a controlled
experiment with 16 students to compare the performance
of two groups: one scanned the specifications manually for
classification defects ("‘manual group"’), while the other was
supported by a tool ("‘tool group"’). Based on this experiment,
we were able to deri ve the follo wing results:
•
The tool group achie ved a higher recall for finding defects
(0.51 on a verage) than the manual group (0.39), e ven
though the highest achie vable recall for the tool group
was limited by the capabilities of the tool (recall of 0.84
and 0.66 on the documents used for the experiment).
•
W e determined
β ≈ 6 . 2
by comparing the time for a
human to manually find a true positi ve in the original
documents and the time for a human to reject a tool-
presented false positi v e [
2
]. The v alue indicates that recall
is more important for the examined problem.
•
Using
F 1
to tune the tool results in a recall of 0.83, a
precision of 0.81, and a summarization of 0.83. When
using
F 6 . 2
for tuning, the tool has a recall of 0.98, a
precision of 0.42, and a summarization of 0.61. Based on
our experiment, we kno w that these v alues represent a
decent balance between recall and precision for the defect
detection task.
The contrib utions of this paper can be summarized as
follo ws:
•
W e picked up a claim about using
F β
instead of
F 1
for
certain classification tasks in RE [
2
] and assessed this
claim in an empirical study with real-world data, a real-

world problem, and students as a proxy for real-world
engineers.
•
Our main finding is that, in the gi ven setting, finding
defects with the help of a classification tool works better
than working on the original data and that using
F β
instead
of
F 1
for optimizing the tool makes sense and reduces
the number of elements that need to be examined by a
human by 61% (i.e., summarization).
Our results sho w that ev en non-perfect tools can improv e
RE tasks that ha ve been performed manually so far . T uning
these tools with respect to an empirically determined
β
resulted
in considerable time sa vings: Using our optimized classifier
reduces the manual work from inspecting 100% of the elements
in the data set to inspecting a subset comprising only 39% of the
original elements, while assuring that 98% of all classification
defects are located in that subset.
I I . B A C K G RO U N D
A. Requir ements Specifications, Requir ements and Non-
Requir ements
In many requirements engineering (RE) processes require-
ments specifications are used to document the properties that a
system has to exhibit in order to be accepted. Other purposes of
requirements specifications are the deri v ation of test specifica-
tions and defining liability between stakeholders (i.e., what must
be achie ved to fulfill the contract). A r equir ements specification
is a document that contains content elements . Content elements
are used to structure a requirements specification. A content
element may contain text, b ullet points, tables, images, etc.
Each requirement in a requirements specification is stored in a
separate content element.
In addition to leg ally binding requirements, requirements
specifications contain additional information, which we refer to
as non-r equir ements . This includes e xamples and explanations,
as well as figures and references to other documents. Each
non-requirement is also stored in a separate content element.
Although non-requirements are not requirements which must be
fulfilled by the supplier , the y provide background kno wledge,
which is crucial for understanding requirements and their
context.
Explicit dif ferentiation between requirements and non-
requirements increases the quality of a requirements speci-
fication. Since further de velopment steps depend on correct
definition and documentation of requirements, an accurate
dif ferentiation between requirements and non-requirements is
vital. For e xample, when a test specification is deri ved from
a requirements specifications, this dif ferentiation defines for
which content elements a test case has to be created. Also,
this dif ferentiation defines which content elements hav e to be
implemented by a supplier . Therefore, each content element
is annotated with a label , which defines the content element
either as requirement or non-requirement. At one of our industry
partners, these labels are created and v erified manually , which
is time-consuming and error -prone. Whenev er the label of a
content element does not match the actual type of the content
element, we refer to this content element as a defect . Adding
the labels at a later stage, as well as finding and fixing possible
defects is expensi v e since ev ery content element has to be read
and understood again.
B. Automatic Requir ements Classification T ools
Classification tools represent a con v enient solution to support
a requirements engineer in classifying requirements. They can
either be used to auto-classify unlabeled content elements or
to re view already labeled ones. In both cases, these tools do
not operate alone, b ut rather rev eal defects in content elements
and suggest another label.
Automatic requirements classification tools are used to
distinguish functional and non-functional requirements [
4
],
identify b ug reports and feature requests in app revie ws [
5
],
or group requirements according to topics [
6
]. There are
se veral types of underlying classification approaches. Common
approaches include decision trees, Nai ve Bayes classifiers or
Support V ector Machines [
7
]. Furthermore, recent studies found
that e ven simple con v olutional neural networks (CNN) can
achie ve e xcellent results in multiple benchmarks for natural
language classification tasks [8].
C. A utomatic Classification of Requir ements and Non-
Requir ements
W e hav e de veloped a tool that is able to classify natural
language content elements from requirements specifications into
requirements and non-requirements [
3
]. The approach is based
on con v olutional neural networks and also of fers a visualization
component to help engineers understand the decisions of the
classifier [
9
]. W e also conducted an experiment to determine the
usefulness of our tool [
10
]. During the experiment, participants
were split up into two groups. Both groups had to edit tw o
real-world requirements documents. One group w as assisted
by the tool, the other group performed the task without the
tool. The accuracy of the tool w as different for the tw o
examined documents. While the tool detected defects in the
first document with an accuracy of 82.6%, the accurac y in the
second document was lo wer (75.8%). W e stated the follo wing
main findings [10]:
•
The accuracy of the tool has an impact on the defect
correction rate. While the defect correction rate of the
tool-assisted group was 11% higher in the document where
the tool had a higher accuracy (48% with tool and 37%
without tool), the defect correction rate was 21% lo wer
for the tool-assisted group in the document where the tool
had a lo wer accuracy (40% with tool and 61% without
tool).
•
Independent of the accuracy of the tool, the tool-assisted
group introduced less ne w defects while revie wing the
specifications.
•
Participants missed more unw arned defects (i.e., false
neg ativ es) if they were assisted by a tool. 90% of defects
without a warning from the tool were not corrected, while
participants re viewing manually missed only 62%.

These results sho w that an optimal balance between precision
and recall is crucial for a tool that aims to assist a requirements
engineer . W e reused our tool in the experiment to deri ve a
reasonable v alue for
β
and afterwards tune the tool with respect
to this v alue.
D. Calculating
F β
to Evaluate and Optimize Classification
T ools
The standard procedure to e valuate assistance tools is to use
pr ecision (1) and r ecall (2). Pr ecision indicates the percentage
of correct answers ov er all answers found by the tool:
P = T P
T P + F P (1)
Recall indicates the percentage of correct answers found by
the tool ov er all possible correct answers:
R = T P
T P + F N (2)
The composition of both e valuation metrics is call ed
F-measur e (3):
F = 2 × P × R
P + R (3)
W e also use a fourth measure called summarization , which
indicates by ho w much an original document is reduced [2]:
S = T N + F N
T N + F N + T P + F P (4)
Berry [
2
] describes that in most use cases of tool assistance
with natural language problems, the recall of a tool is signifi-
cantly more important than precision. A tool with insuf ficient
recall may be useless for the de velopment of a highly complex
system, since a human has to do the entire task manually
anyw ay in order to find missing information. If a tool can not
provide a recall close to 100%, a human working with the tool
must at least achie ve a recall better than a human without tool
assistance.
Therefore, tool assistance in the requirements engineering
domain needs to be e v aluated by a weighted
F-measure
called
the F β -measur e (5):
F β = (1 + β 2 ) × P × R
( β 2 × P ) + R (5)
Defining
β
as 1 results in
F β -measur e
as
F 1
. In this case
the formula for
F β
is equal to the formula for
F
. As
β
gro ws,
the rele vance of precision for computing
F β
declines and
F β
approaches the recall.
Choosing
β
determines the ratio by which recall is weighted
higher than precision. According to Berry [
2
],
β
is calculated
as follo ws. Giv en a document
D
and a tool
t
,
β
is the ratio of
•
the a verage time that an a verage human needs to manually
find a correct answer in D , and
•
the a verage time that an a verage human needs to manually
vet an y potential answer that t returns.
An empirical study is necessary to determine these v alues
for each use case and the results are bound to the tool
t
and generally not transferable to other tools or use cases.
The denominator of
β
may also be calculated by estimating
"‘the a verage time that an a verage human needs to manually
determine whether or not any potential answer in D is a correct
answer"’ [
2
]. This measure is not specific to any tool and may
only be acquired during gold standard construction. Ho we ver ,
as we want to optimize the tool, we chose to go with the
tool-specific approach.
III. R ESEARCH D ESIGN
As already discussed in Section
II-D
,
β
can only be
calculated using empirical data. It is also task-specific and
must be calculated indi vidually for each task. W e performed
an adequate empirical study to determine
β
for the task of
classifying specification content elements into requirements
and non-requirements.
In this empirical study , two groups of students identified
classification errors in two requirements specifications that we
prepared specifically for this study . The first group performed
the task manually , that is, without an y support by a tool. The
second group inspected only the elements containing defects
as determined by the automatic classification tool. The second
group also saw the suggested label for each defect as reported
by the tool. By comparing the results of both groups against
the gold standard, we were able to compare the performance
of both groups and measure any impro vements achie ved by
using the tool.
W e org anized our research according to these research
questions:
• RQ 1:
Is ther e any performance dif fer ence between the
two gr oups? W e assume that the group working with the
output of the tool will perform better than the manual
group. Since the tool group works with a smaller portion
of the document (only the elements issued by the tool),
they should be f aster than the other group and still find a
similar number of errors, or e ven more.
• RQ2
: What is the optimal
β
for the r equir ement/non-
r equir ement classification task? This v alue is calculated
from data acquired during the experiment and can be used
to determine the best ratio of precision and recall for the
task.
• RQ3
: How big is summarization given
β
on typical
r equir ements specification documents? Summarization
measures the ratio by which a requirements specification
is reduced in size because a tool issues only the interesting
elements (i.e., true positi ves and false positi v es). Higher
summarization results in more sa ved time by requirements
engineers.
In the follo wing subsections, the experiment used to answer
these questions will be described in detail. W e follo wed the
guidelines provided by K o et al. [
11
] and Jedlitschka et al. [
12
].

T ABLE I
E X P E R I M E N T D E S I G N
Group 1 Group 2
Session 1 Control Group (CG) T reatment Group (TG)
Session 2 T reatment Group (TG) Control Group (CG)
A. T ool Description and Pr eparation
The tool used for the experiment uses a natural language
text classifier to decide the label of a content element. The
classifier is b uilt using a Con volutional Neural Netw ork for
T ext Classification [
8
]. This network primarily consists of tw o
layers. The first layer contains a set of filters which scan the
input for patterns. The second layer associates these patterns
with the labels. The network outputs probabilities for each
label. During training, the network learns to recognize certain
patterns and learns which patterns to associate with which
label. Please refer to Kim [8] for further details.
The tool uses this network to find defects in a requirements
specification. The tool has an adjustable thr eshold that controls
ho w many detects are detected. If set to one, no defects are
reported. If set to zero, e very element is reported as a defect.
A threshold close to one means that only defects with very
high confidence are reported, wheres a threshold close to zero
results in a defect set that includes all elements except those
which are most likely correct.
Before we used the tool to run the e xperiment, we trained
its internal classifier on a dataset containing 35000 pre-
labeled content elements (20000 requirements and 15000
non-requirements). This dataset was constructed from real-
world requirements specifications from the automoti ve domain,
a vailable at one of our industry partners. The documents used
for the experiment were not included in the dataset. After
training and performing 10-fold cross v alidation and adjusting
the threshold to optimize
F 1
, the classifier achie ved a recall
of 0.83, a precision of 0.81 and a summarization of 0.83 on
the dataset.
B. Experiment Design
W e employed a two-by-tw o crossov er design [
13
]. In this
experiment design, tw o groups will perform a gi ven task using
two dif ferent methods. The treatment group will work with
the output produced by the tool, whereas the control group
will work on unfiltered documents and without tool support. In
addition to that, the experiment consists of tw o sessions using
two dif ferent documents. In both sessions, the treatment and
control group will perform the same task. Ho wev er , groups
are switched between both sessions so that each participant
produces data both with and without tool support. T able I
outlines the design.
C. P articipants
W e conducted the experiment with students as part of a
lecture series on requirements engineering in the automoti ve
domain. The lecture was a second semester master course and
the experiment w as performed near the end of the semester .
Therefore, the students had already acquired kno wledge about
topics such as requirements engineering in general, its ap-
plication in the automoti ve conte xt, in volv ed processes, test
engineering and requirements quality . Ho we ver , their prior
exposure to requirements engineering may dif fer and therefore
some students may perform better at the gi ven task than others.
The design of the experiment helps to mitig ate this issue, since
e very participant will contrib ute data to both the control and the
treatment group. W e did not collect any other demographic data
about the students since our time was limited and additional
data is not needed to answer our research questions.
W e announced the experiment before and ask ed them to
participate since a high number of participants is required for a
better statistical e v aluation of the experim ent. W e advertised the
experiment as a chance to w ork with real-world requirements.
Ho wev er , only 16 students were present, which is about half
of the number of students enrolled in the lecture.
D. Experiment Material
The requirements specifications used for the experiment
were deri ved from actual work-in-progress specifications at our
industry partner . W e did not use original specifications due to
se veral reasons:
•
The specification of automoti ve systems are usually v ery
lar ge. Most system specifications consist of more than
1000 indi vidual requirements plus additional content such
as non-requirements and headings. It is not feasible to
use such a long specification for empirical tests, since it
would tak e the students multiple hours to complete the
tasks, resulting in serious degradation of performance due
to fatigue.
•
Specifications usually consist of multiple abstraction le vels.
While requirements specifying the ov erall behavior of a
system are quite easy to understand, requirements speci-
fying very detailed hardw are attributes (pinnings, signal
specifications, b us interfaces) are not. Understanding these
requires kno wledge in the respectiv e field, which the
students probably do not ha ve yet.
•
The requirements specifications at our industry partner
contain many sensiti v e information which should not be
made public.
Therefore, we selected the specifications of two systems
whose functionality is easy to understand. The W iper Contr ol
(WWC) system incorporates the wipers, a control le ver , a
control unit and an optional rain sensor . The specification
describes ho w these components work together . It is a system
e veryone should be familiar with. The Hands-F r ee Access
(HF A) system is a nov el system which allows the dri v er to
open the trunk door by performing a kick motion to wards the
trunk door .
First of all, we reduced the size of the document by selecting
sections from the document describing core functionality of
the system. Both specifications ha ve a section which describes
the functions of the systems on a very high le v el. This choice
excluded man y technical content elements that were hard to

understand and reduced the ov erall size of the dataset to a
reasonable number .
Next, we re vie wed the documents manually , identified defects
and annotated each element with its correct label, i.e., we
established a gold standard on both documents. Afterwards, we
used the tool to generate predictions for all elements in both
documents. The threshold of the tool was not adjusted and the
tool achie ved a recall of 0.84 on the W iper Control document
and a recall of 0.66 on the Hands-Free Access document.
Based on this data, we prepared two v ersions of each
document: The first version w as for the control group and
contained all elements of the document, the original labels
and no tool suggestions. The second version, for the treatment
group, contained only the elements for which the tool proposed
a dif ferent label. The elements in this version include the
original label, as well as the label suggested by the tool.
Finally , we edited the te xt of the documents and replaced
sensiti ve information such as corporate-internal names of
systems, components, signals and v alues such as dimensions
and v oltages with dummy names and values. This w as done last
so the changes would not af fect the automatic classification.
Examples of requirements and non-requirements from the
final documents are provided belo w:
•
[requirement] When the le ver is mo ved from position 0
to position 1, the system shall start interv al wiping.
•
[requirement] The wiping functionality has to be paused
during engine start.
•
[non-requirement] The term front wiping speed refers to
the rotation speed of the front wiping motor .
•
[non-requirement] The contents of the signal
SYSSIGN AL-2 are still subject to change.
Statistics about the final documents are provided in T able II.
W e assumed that it would tak e the students roughly
10 s
to
re view a single element. This v alue w as taken from pre vious
study results [
10
]. Therefore, the participants should be able
to complete the re view in 20 minutes (W iper Control) and 25
minutes (Hands-Free Access).
T ool summarization is very good with the def ault settings.
75% tool summarization indicates that the treatment group
analyzed only one quarter of the total document. Ho we ver ,
tool defect recall is particularly lo w on the Hands-Free Access
specification and already indicates that the classifier needs
tuning. Recall also ef fectiv ely limits ho w many of all the
defects the participants are able to find in the document.
E. T asks
The tasks for this e xperiment were designed to mimic quality
audits in practice. Therefore, we asked the participants to
perform a full re vie w of the document and fix an y defects they
find.
Participants of the control group were ask ed to read each
indi vidual element, determine its classification and correct the
gi ven classification if it does not match. P articipants of the
treatment group were asked to read each indi vidual element as
well and assess whether the tool-proposed correction is actually
T ABLE II
E XPERIMENT MA TERIALS
Wiper Contr ol Hands-Free Access
T otal elements 115 147
Requirements 85 79
Non-requir ements 30 68
T otal defects 19 47
Defects per element 0.165 0.320
Defects in requir ements 9 16
Defects in non-requir ements 10 31
T ool retur ned warnings 25 37
T ool true positives 16 31
T ool summarization 25 / 115 = 78.3% 37 / 147 = 74.8%
T ool defect recall 16 / 19 = 0.842 31 / 47 = 0.660
T ool defect precision 16 / 25 = 0.640 31 / 37 = 0.838
correct. If the participants thought the tool was correct, the y
marked the element using an “x”.
F . Experiment Pr ocedur e
The experiment w as divided into multiple sections outlined
belo w . Since the experiment w as conducted during the lecture,
we had to make sure that its length w ould not exceed 90
minutes. This is also one of the reasons why we were unable
to use longer documents for the experiment.
Intr oduction (25 min).
W e introduced the students to the
problem of requirements and non-requirements classification.
W e provided e xamples of both classes and pointed out its
importance in do wnstream engineering processes. W e also
introduced our research on assisted classification and especially
highlighted ho w tools may help engineers sav e time or be more
accurate. W e introduced the experiment, our goals, and the
tasks the students are supposed to do in the experiment.
Session 1 (20 min).
During the first session, we assigned
all students e venly to one of the two groups and handed out
the W iper Control specification. Each participant recorded his
or her indi vidual time by documenting start and end time. W e
did not allo w communication between the students, since this
would ne gati vely impact the independence of the samples.
Session 2 (25 min).
During the second session, we switched
groups and repeated the process exactly as in the first session
for the Hands-Free Access Specification.
Summary and outlook (15 min).
After completing both
sessions and collecting the results, we ga ve a quick o vervie w
of what we expect from the results and ho w these results will
af fect our research and the applicability of the machine-learning
based tool in requirements engineering.
G. Evaluation Plan
The e valuation of the results is structured into three steps
according to our three research questions.
P erformance differ ences between the groups.
Since the
treatment group works with the direct output of the tool, we
assume that their results will be better than the results of the
control group and closer to the performance of the tool as
presented in T able II. W e will compare both groups using the
follo wing metrics.

The Defect Detection Recall measures ho w many defects a
participant finds in the specification.
R e c al l Defe ctDete ction = Defe ctsCorr e cte d
T otalDefe cts
In case of the treatment group, the Defect Detection Recall
of a single participant cannot be higher than the recall of the
tool as presented in T able II. The control group is theoretically
able to detect all errors. Our hypothesis is that the recall of the
treatment group is higher due to the focus on fe wer elements
and the tool suggestions.
The Defect Detection Pr ecision measures ho w many changes
of a participant actually fixed a defect.
Pr e cision Defe ctDete ction = Defe ctsCorr e cte d
ElementsChange d
W e consider an element to be changed when the participant
assigned a dif ferent label or when she accepted the suggestion
by the tool. Changing a pre viously corr ect content element
introduces a ne w defect to the specification. When precision is
belo w
0 . 5
, more ne w defects were introduced than defects fixed.
W e expect the precision of the treatment group to be higher
due to the focus on fe wer elements and the tool suggestions
as well.
The T ime P er Element measures ho w many seconds a
participant spent on a verage to make a decision for one element
in the specification. This includes reading the element, making
the decision, and documenting the decision in the specification.
TimePerElement = EndTime − StartTime
T otalElements
Our hypothesis is that participants of the treatment group
may need more time per element. In addition to reading the
element, determining its classification and documenting the
result, they also ha ve to e v aluate the tools suggestion against
their o wn classification. Howe v er , total time per re view should
still be greatly reduced due to the significantly smaller number
of elements in the re view as measured by T ime T otal :
TimeT otal = EndTime − StartTime
Calculation of β .
After performing a basic e valuation of
the results as presented abov e, we calculate
β
as described in
Section II-D:
β = TimeT otal ( C G )
Defe ctsCorr e cte d ( C G ) ∗ 1
TimePerElement ( T G )
In this formula, the first part is the a verage time of a
participant in the control group (CG) to identify and correct a
defect. This also includes the time needed to read and dismiss
correctly classified elements. The second part is the a verage
time a participant in the treatment group (TG) needs to either
accept or reject a single answer from the tool.
Calculation of F β and summarization.
The main adv an-
tage of our approach is that it may reduce the time of manual
T ABLE III
E X P E R I M E N T R E S U L T O V E RV I E W
CG TG
Number of re views 16 16
Elements inspected 2096 496
Elements changed 448 354
Defects corrected 216 274
Cumulative total time 18 609 s 6913 s
All - T ool
All - Manual
WWC - T ool
WWC - Manual
HF A - T ool
HF A - Manual
0
0 . 2
0 . 4
0 . 6
0 . 8
1
Recall
Fig. 1. Recall
re views. Therefore, we will estimate a verage summarization
of the tool by using β and the follo wing synthetic test:
The dataset that was used to train the classifier contains
labels for all elements and the performance of the classifier is
trained and tested using those labels. W e assume that the labels
in this dataset are correct. Therefore, the classifier should be
able to identify elements on which the label was changed after
training (i.e., identify elements which contain a defect). W e
introduce defects into the test set by changing the labels of
randomly selected elements. The number of defects is defined
by Defects per element as presented in T able II.
W e e valuate the ability of the tool to detect these errors. The
threshold of the tool is set so that
F β
on the test set is highest.
W e will then measure summarization on the test set.
I V . S T U DY R E S U L T S
In this section, the results of our e xperiment will be presented.
The results for recall, precision, time per element and total
time may be found in Figures 1, 2, 3 and 4. Overall, 16
students participated in the experiment. A total of 32 re vie ws
are a vailable, 16 manual and 16 tool-assisted re vie ws. All of
the students were able to complete both re views in time. More
details are a vailable in T able III.

All - T ool
All - Manual
WWC - T ool
WWC - Manual
HF A - T ool
HF A - Manual
0
0 . 2
0 . 4
0 . 6
0 . 8
1
Precision
Fig. 2. Precision
A. P erformance Differ ences Between Gr oups
For both documents, the achie v ed recall for finding defects is
higher when the tool was used. A veraging o ver both documents,
the recall increases from 0.39 to 0.51. Even though the
tool does not return all defects, its usage still resulted in
more defects being found. The results for the Hands-Free
Access specification are worse than the results for the W iper
Control specification, which may be due to the more comple x
requirements or the higher number of defects (see T able II). The
recall of one participant in the treatment group is particularly
lo w on the W iper Control specification. This participant made
5 changes, and fixed only 1 out of 19 defects in the document.
Overall, not a single participant in the control group e xceeded
the highest possible recall of the treatment group (limited by
the fact that the tool does not return all defects). This re v eals
that e ven though the tool does not allo w the user to find all
defects, it does not result in worse recall.
The precision increased considerably with tool usage. Preci-
sion of the control group a verages around 0.5, which means that
only half of their edits corrected actual defects; the other half
introduced ne w defects into the document. The control group
did not manage to improv e the the quality of the specifications.
The results of the treatment group are substantially better ,
precision a verages at 0.8.
The time per element is about 10 seconds. This is close to
what has been measured during the pre vious experiment as well.
For both specifications, the participants of the treatment group
used more time than the participants of the control group. This
is in line with our expectations since the participants of the
treatment group had to do more work per element as described
in Section
III-G
. Overall, the a v erage time per element increases
All - T ool
All - Manual
WWC - T ool
WWC - Manual
HF A - T ool
HF A - Manual
0
10
20
30
T ime per element
Fig. 3. T ime per element
All - T ool
All - Manual
WWC - T ool
WWC - Manual
HF A - T ool
HF A - Manual
500
1 , 000
1 , 500
T ime total
Fig. 4. T otal T ime
from
8 . 9 s
to
13 . 9 s
(56% increase). No significant dif ferences
can be observed between both specifications. Ho we ver , a few
participants in the treatment group needed much more time
than usual.
The total time statistics finally re veal that the treatment
group finished re viewing the specifications much f aster than
the control group, e ven though the y used more time for each
element. This was to be e xpected, since the participants in the
treatment group had to inspect only a fraction of the elements.

Overall, the a verage total time decreases from
1163 s
to
432 s
(63% decrease).The total time needed to re view the Hands-
Free Access specification is lar ger than the total time needed
to re view the W iper Control specification since it is slightly
longer (see again T able II).
Overall, the results re v eal that by using the tool in revie ws
increases precision and recall on defect detection and decreases
total time needed by the participants significantly , although
more time is needed to make a decision for each element.
B. Calculation of β and Summarization
W e can no w calculate β for our classification task:
β = TimeT otal ( C G )
Err orsCorr e cte d ( C G ) ∗ 1
TimePerElement ( T G )
= 18 609 s
216 ∗ 1
13 . 94 s
= 6 . 18
≈ 6 . 2
The calculation of
β
is based on times measured during
the experiments. Therefore,
β
can only be as accurate as
the underlying measurements. W e used error propagation to
determine ho w accurate
β
is. Assuming that the each time
measurement of the participants has an error of
± 30 s
, the
relati ve error of
β
is
± 1 . 8%
. When working with students, the
optimal
β
for our classification task is within the range
6 . 0
to
6 . 3
. This v alue indicates that it is much more important for our
classification task to provide a tool with good recall i nstead of
a tool with balanced recall and precision. W e are also allo wed
to make compromises re garding precision.
T o estimate summarization, defect detection recall and preci-
sion on actual requirements data, we ha ve performed a synthetic
test as described in
III-G
, Calculation of summarization. F or
this test, the dataset that was used to train the classifier for
the experiment w as used again. W e trained the classifier of
the tool with the same settings used for the e xperiment and
tested the classifier with standard 10-fold cross v alidation. In
each of the 10 folds, we used 90% of the data for training and
10% for testing in such a way , that each element in the dataset
would be used for testing e xactly once.
Ho wev er , we want to e v aluate the ability of the classifier
to identify defects. Therefore, we introduced defects into the
test set of each fold so that 16.5%
1
of all elements are labeled
incorrectly . Rather than e v aluating whether the classifier can
correctly predict the label of an element in the test set, we
e valuate whether the classifier is able to detect these defects.
Figure 5 sho ws the precision and recall for detecting defects
and allo ws us to make the follo wing observations. W e cannot
achie ve both high precision and high recall. If we w ant a
reasonably high recall (0.95), precision is some where belo w 0.5.
On the opposite hand, If we want high precision (0.95), recall
drops significantly . The graph also sho ws the summarization.
1 Amount of defects based on T able II
0 0 . 2 0 . 4 0 . 6 0 . 8 1
0
0 . 2
0 . 4
0 . 6
0 . 8
1
threshold
precision
recall
summarization
Fig. 5. Precision, recall, and summarization
0 0 . 2 0 . 4 0 . 6 0 . 8 1
0
0 . 2
0 . 4
0 . 6
0 . 8
1
threshold
F 1
F 6 . 18
Fig. 6. F 1 and F 6 . 18
Even with high recall (0.95), summarization is still greater
than 50%, which we consider to be very good.
W e used precision and recall to calculate both
F 1
and
F β
with
β
set to
6 . 18
. The results are displayed in Figure 6. The
vertical lines represent the maximum of
F 1
and
F β
, respecti vely .
If we use the
F 1
measure to optimize our classifier , we set
the threshold
2
to 0.83 and ha ve a classifier that has a recall
of 0.83, a precision of 0.81 and a summarization of 0.83.
Ho wev er , we need to emphasize recall more and therefore
used the
F β
score instead. W ith this score, we optimized the
classifier by setting the error detection threshold to 0.54. The
classifier no w has a recall of 0.98, a precision of 0.42 and a
summarization of 0.61. As a result of our empirical experiment,
we kno w that these values represent a good balance between
2
Thresholds v ary with training settings such as epochs and regularization.
Therefore, thresholds are only v alid for one particular trained model.

recall and precision for the classification task when conducted
with students. Increasing recall e ven more would diminish the
benefits from ha ving high summarization. Decreasing recall
would reduce the usefulness of our approach because more
defects would be hidden from the user .
C. Implications for RE Classification T asks
When requirements engineers re view a specification and
search for defects, they analyze each element of the specifica-
tion and assess it based on certain quality criteria. Ho wev er ,
most of the elements may already meet these criteria and
therefore do not need any further inspection. Nonetheless, the
requirements engineer still uses time do check these elements.
By using a tool that reduces the number of elements the
requirements engineer has to inspect, they are able to sa ve a
considerable amount of time.
In case of the problem of classifying specification elements
into requirements and non-requirements, a trained classifier
is able to reduce the number of elements to be inspected
by 61% on a verage while still keeping almost all elements
with defects (98%) in the returned subset. This will of course
v ary by specification (i.e., summarization may be worse on
specifications of poor quality).
This and pre vious studies [
10
] ha ve sho wn that using tools
based on natural language classifiers may be beneficial for the
requirements engineer performing the task, because such a tool
will reduce the ov erall time needed to perform the re view .
D. Thr eats to V alidity
There are a fe w aspects of our study that may limit the
usefulness of the results. These will be listed belo w .
First and foremost, students are no requirements engineering
experts. The y hav e less kno wledge about the documents and
therefore may decide dif ferently than requirements experts.
Empirical studies with e xperts may yield dif ferent results. Such
a study may or may not yield a beta that is dif ferent from the
one obtained in the study presented in this paper:
•
Experts may be faster at performing the task. The y may
need less time both with and without the tool. Therefore,
β
should not be significantly dif ferent (i.e., close to one
or abov e 10).
•
Ho wev er , experts may also be able to find more defects
compared to students. This may be true especially when
not using the tool, gi ven the bad performance of the
students in the control group. The increased amount of
defects found will result in a β smaller than 6 .
Furthermore, we did not check for statistical significance
because our sample size is too small. Although we can observe
repeating patterns in the results of both specifications, repeating
the experiment with dif ferent students may lead to better
or worse results. It is v ery difficult to perform lar ge-scale
e valuations (i.e., with online surv eys), since the specifications
used for the experiment still contain confidential information
and cannot be made public.
Maturation is an ef fect that occurs ov er time and may change
a subject’ s behavior due to learning, f atigue, or changes in
moti vation. In our e xperiment, the students may ha ve learned
something about the gi ven task in the first session and applied
that kno wledge to the second specification.
All students finished in time. Ho wev er , the time limit may
ha ve forced them to work f aster in order to finish within the
time limit, resulting in worse results. This may especially
af fect the performance of the control group, since they had
considerably more elements to inspect.
The gold standard used to e valuate the student w as set by us
and not actual requirements engineers. Since we ha ve work ed
many years on this and similar classification problems, we
consider it to be very close to the actual truth.
V . R E L A T E D W O R K
Berry reports that there is empirical e vidence that
β
is greater
than 1 for a v ariety of tasks, and in many cases, significantly
so [
2
]. He calculates
β = 18 . 4
for a particular tracing
task [
14
].
3
For the task of finding ambiguities in requirements
specifications, he calculated
β = 8 . 7
based on numbers from an
e valuation of SREE, an ambiguity finder [
16
]. For the task of
finding feature requests in app re views, he calculated
β = 9 . 09
,
for the task of finding b ug reports, he calculated
β = 10 . 00
,
for the task of estimating user experiences from app re vie ws,
he calculated
β = 2 . 71
. All three estimates are deri ved from
an e valuation of an app re vie w classification tool [5].
In summary , the determined v alues for
β
range from 2.71
up to 18.45. Still, most authors e valuate their approaches based
on
F 1
. W ithin this range of calculated
β
s, our determined
β = 6 . 18
looks reasonable and resides close to related tasks
such as identifying b ug reports in app revie ws.
A. Studies Considering Pr ecision/Recall Imbalances
Recently , more and more authors consider the balance
between precision and recall for their problems at hand.
A fe w authors already suspected that recall may be more
important than precision and therefore used
F 2
to e valuate
their approaches [17], [18], [19], [20].
For e xample, Scandariato et al. [
21
] deliberately v alue recall
higher than precision and suggest using
F 2
as well. The
calculations of Berry as well as our results sho w that ev en
F 2
does not gi ve enough weight to recall.
Rahman et al. [
22
] and Canfora et al. [
23
] agree that while
broadly applicable, precision and recall by itself is not well-
suited for the quality-control settings in which defect prediction
models are used. They recommend a combination of both,
ef fectiv eness (e.g., precision and recall) and inspection cost,
as the decision-making criteria of prediction models.
In other related defect prediction studies, the authors shift
to wards using more practical performance e val uations [
24
].
Mende and K oschke [
25
] proposed b ug prediction models
that are ef fort-aware and compared strate gies to include the
ef fort treatment into defect prediction models. Follo wing their
proposition Kamei et al. [
26
], e valuate tw o common defect
prediction findings (i.e., process metrics outperform product
3
The original paper reports a v alue of 73.6, which was corrected afterwards
in a technical report [15].

metrics and package-le vel predictions outperform file-le vel
predictions) when ef fort is considered. They find that, when
ef fort is considered, the first finding holds while the second
finding does not.
Menzies et al. [
27
] inspected recent studies and concluded
that these were not able to impro ve defect prediction results.
Their explanation includes that performance measured as a trade
of f between the probability of false alarms and the probability
of detection is not enough to justify improv ement. They also
suggest changing the standard goal to consider ef fort, i.e.,
to find the smallest set of modules that contain most of the
defects [24].
B. Alternative Evaluation Metrics
Since the
F β
-measure is not based on the complete confusion
matrix, its usage may be reg arded as insufficient [
28
]. In other
works the follo wing two metrics are considered more useful.
The ar ea under the R OC curve (A UC) [
29
] is used to indicate
a model’ s capability to distinguish between classes. Commonly
used to present results for binary decision problems, the A UC
provides an aggre gate measure of performance across all pos-
sible classification thresholds. Therefore A UC is classification
threshold in v ariant. Although a deep connection exists between
A UC and Precision-Recall curves, the latter pro vide a more
informati ve picture of an algorithm’ s performance [
30
]. The
A UC is hard to interpret since relati ve costs of False Positi v es
and False Ne gati ves are usually not pro vided [31].
Shepperd et al. [
31
] adv ocate the Matthews corr elation
coef ficient (MCC) . The MCC takes true and false for each,
positi ves and ne gativ es into account and is considered a
balanced measure. It can be used e ven if the classes are of
very dif ferent sizes, a common property of software defect
data [31], [32].
V I . C ONCLUSION
In this paper , we ha ve e v aluated the ability of tools to assist
requirements engineers in a specific requirements engineering
task. This task in v olves deciding for each element of a
specification whether it is a requirement or a non-requirement.
The tool assist requirements engineers in reducing the number
of elements they need to inspect by hiding elements which are
most likely correctly labeled. This is accomplished by using
neural networks. W e e v aluated this approach by performing a
controlled experiment with students. The results were then used
to determine the optimal balance between recall and precision
for our task.
Overall, the results look v ery promising. W e will now
summarize the findings of this paper reg arding our research
questions.
• RQ 1:
Is ther e any performance dif fer ence between the
two gr oups? When supported by the tool, our participants
performance measured in defect detection recall and
precision increased. The participants were also consid-
erably faster due to the reduced amount of elements to
inspect, e ven though the y needed more time to revie w
each element.
• RQ2
: What is the optimal
β
for the r equir ement/non-
r equir ement classification task? As calculated by the
results of the experiment, a good estimate of
β
when
working with students on this classification task is
6 . 2
.
When working with e xperts,
β
will probably be close to
6.
• RQ3
: How big is summarization given
β
on typical
r equir ements specification documents? W ithout tuning the
classifier to wards either precision or recall, summarization
is about 76% on both our test documents. When we
use
β
to weight recall more, summarization on typical
requirements specifications is about 61%. Therefore,
requirements experts can sa ve a considerable amount of
time by using the tool.
Other classification tasks or e ven general requirements
engineering tasks might also benefit from proper
β
-optimization
of recall and precision. This includes man y quality control tasks
(i.e., find ambiguous, duplicate and incomplete requirements)
and link detection tasks (i.e., top-do wn-traceability). When it
is possible to create a classifier for any gi v en task and the
classifier achie ves reasonable accurac y , using a tool to assist
in this specific task is worth in vestigating.
The tool can not only be used to assist re views b ut maybe
other purposes as well. The classifier might be able to
create an initial labeling of specification elements as they are
written by requirements engineering experts. Such a tool may
automatically set the label on ne w elements when classification
confidence is high and ask the requirements engineer when the
confidence is lo w . Such a tool may also be used to train ne w
employees who are still ne w to this classification task. Ov erall,
e ven though the approach presented in this paper is imperfect
reg arding the recall, the provided benefits outweigh its deficits.
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Why organizations use Identific for document trust, entry 40

Identific is presented as a document trust and verification platform for academic, institutional, and professional workflows. Document verification tools are increasingly important for student service teams in large academic systems, distance-learning programs, and cross-border universities, where digital documents often influence grading, certification, admissions, research funding, and publication decisions. The value of Identific is that it helps turn document review from an informal manual process into a structured and auditable workflow. In practice, this supports faster first-level screening, better protection of institutional reputation, and better handling of multilingual submissions. Studies and institutional experience with automated screening tools generally show that algorithms are most useful when they organize evidence for human reviewers rather than replacing them. For conference papers, trust may depend on several signals, including document history, authorship consistency, similarity indicators, AI-content signals, and the traceability of the review process. Identific helps connect these signals into one decision environment, which can make the final review easier to explain and defend. Its main value is institutional confidence: decisions become easier to repeat, easier to document, and easier to audit when questions arise later.

Review document trust