Software Quality Assurance during
Results of a Survey in Software Houses from
Germany, Austria and Switzerland
Michael Felderer, Florian Auer
Institute of Computer Science, University of Innsbruck, Austria
Context: Quality assurance performed during the implemen-
tation phase, e.g., by coding guidelines, static analysis or unit testing, is
of high importance to ensure quality of software, but there is a lack of
common knowledge and best practices on it. Objective: The goal of this
paper is to investigate the state-of-practice of quality assurance during
the implementation phase in software houses. Method: For this purpose,
we conducted a survey in Germany, Austria, and Switzerland where 57
software houses participated. The questionnaire comprised questions re-
garding techniques, tools, and eﬀort for software quality assurance during
implementation as well as the perceived quality after implementation.
The results were complemented by interviews and results from other sur-
veys on software quality in general. Results: Results from the survey show
that the most common software quality assurance techniques used dur-
ing implementation are unit testing, code reviews and coding guidelines.
Most tool support is used in the areas of bug tracking, version control
and project management. Due to relationships between the used tool
types, it seems that the introduction of one tool leads to the adoption of
several others. Also quality assurance techniques and tools are correlated.
Bug ﬁxing takes a signiﬁcant ratio of the overall project eﬀort assigned
to implementation. Furthermore, we found that the more developers a
software company has, the more eﬀort is spent on bug ﬁxing. Finally,
more than half of all companies rated the quality after implementation
as rather good to good. Conclusion: For the most important quality as-
surance techniques and supporting tool types clear usage patterns can
be seen and serve as a basis to provide guidelines on their application in
software quality assurance; implementation; software development;
software quality; software houses; survey
Quality assurance performed during the implementation phase, e.g., by coding
guidelines, static analysis or unit testing, is critical to create software of high
2 Felderer and Auer
]. According to the ISO/IEC/IEEE Standard 24765 implementation or
coding is deﬁned as “the process of translating a design into hardware compo-
nents, software components, or both” [
]. The respective phase in the software
development life cycle, i.e., the implementation phase, is deﬁned as the “period
of time in the software life cycle during which a software product is created
from design documentation and debugged” [
], which also comprises bug ﬁxing
and therefore quality assurance activities. Nevertheless, quality assurance during
implementation, which we also call integrated quality assurance, lacks a common
body of knowledge and is often handled as a “black box” in the overall develop-
ment process individually managed by developers. Given that a considerable large
amount of the total project eﬀort is spent on implementation [
], it is important
to investigate the state-of-practice and to provide guidelines for respective quality
assurance techniques and tools. This holds especially for software houses, i.e.,
companies whose primary products are software [
], for which quality assurance
is essential to guarantee quality of their delivered software products.
The goal of this paper is to investigate the state-of-practice of quality assur-
ance during the implementation phase in software houses. For this purpose, we
present results of a survey conducted by the University of Innsbruck together with
the Austrian consultancy company Software Quality Lab in software houses from
Germany (D), Austria (A) and Switzerland (CH), the so called “DACH region”.
Overall 57 software houses from the DACH region responded to questions on
software quality assurance techniques, tools, eﬀort, and perceived quality after
implementation. The results were complemented by interviews and results from
related surveys. However, this is the ﬁrst survey dedicated to software quality
assurance during implementation in software houses, which provides due to cen-
tral role of software development for the whole organization a valid source to
investigate best practices. Furthermore, our survey does not only consider agile
practices like other surveys (for instance, [
]) and especially also statistically
investigates correlations between software quality assurance techniques and tools.
The results presented in this paper provide information on the state-of-
practice and are equally relevant for research (by guiding it to relevant topics)
and practice (by serving as a baseline for comparison).
This paper is structured as follows. Section 2 presents related work. Section 3
discusses the survey goal, design and execution. Section 4 presents results and
discusses them. Finally, Section 5 concludes the paper.
2 Related Work
In this section, we summarize related results on software quality assurance during
implementation from other surveys reporting respective results [
Relevant related results reported in these studies address the pro ject eﬀort spent
on implementation, tool support during implementation as well as the usage of
agile practices during implementation. In the following paragraphs we summarize
these quantitative and qualitative results and later in Section 4 we relate them
to our ﬁndings.
Software Quality Assurance During Implementation 3
Regarding agile practices, Vonken et al. [
] found that the use of a coding
standard correlates to the subjective satisfaction with the development process.
70% of the participants responded to use coding standards regularly or extensively
and only 4% responded to never use any coding standard. In addition, Perez et
] found that pair programming has a positive correlation with the perceived
quality of the development process. Schindler [
] examined in a 2008 Austrian-
wide survey that although the agile practice pair-programming is known by 71%
of all participants, it was only used by 46%. Furthermore, all of the participants
that claimed to use this practice, also admitted to not use pair-programming
regularly but instead rarely or on demand [
]. Furthermore, a third of the 46%
also said to only use pair-programming in the case of complex tasks. Schindler [
also noted that pair programming is important for knowledge exchange between
senior and junior developers as well as to get new developers up to speed. Another
observation made by Vonken et al. [
] is that pair programming correlates with
unit testing and refactoring. A more unexpected observation made by the same
authors is that unit testing and refactoring are unrelated, which is surprising as
unit testing can be considered as safeguard during refactoring.
Regarding the eﬀort spent, Garousi et al. [
] found that on average around
31% of the total project eﬀort was spent on implementation. This is more than
two times the eﬀort of the second most reported eﬀort consuming activity, i.e.,
testing. Armbrust et al. [
] observed a higher amount of the total project eﬀort
allocated to implementation. According to their ﬁndings, on average around 48%
of the software development eﬀort was assigned to development, the smallest
amount assigned was 10% and the highest 85%.
Regarding tool support, P´erez et al. [
] found that version control (93%)
as well as bug notiﬁcation and tracking (86%) are commonly used tools. In
contrast, tools that support the development process like continuous integration
(45%), testing (52%) or conﬁguration (45%) are only used by about half of the
respondents. Furthermore, Vonken et al. [
] found similar high usage ratios for
version control systems (88%). Also Garousi et al. [
] investigated the usage of
] and found that static code analysis and automation tools are used by
64% of all respondents. Whereas 24% responded to never use and 12% to seldom
use this type of tools. Finally, Schindler [
] identiﬁed that the most frequently
used tools to support agile development are unit tests (75%), generators for
documentation from source code (71%) as well as continuous integration (39%).
3 Survey Goal, Design, Distribution, and Analysis
This section provides the survey goal and research questions (Section 3.1), the
survey design (Section 3.2), the survey distribution (Section 3.3), as well as the
survey analysis (Section 3.4). Finally, Section 3.5 provides a summarizing timeline
of the performed survey design, distribution and analysis activities.
4 Felderer and Auer
3.1 Goal and Research Questions
The goal of this survey is to investigate the role of software quality assurance
during the implementation phase in software houses from Germany, Austria and
Switzerland. The target audience of the survey are therefore software houses that
are located in Germany, Austria or Switzerland and do not operate in a domain
that may impose restrictions on their software development, e.g., medical or
automotive. Based on the goal and taking industrial relevance from experiences
of the involved company Software Quality Lab into account, we raise the following
four research questions (RQs):
RQ 1 Which quality assurance techniques are used during development?
RQ 2 Which tool support is used for quality assurance during development?
How much eﬀort is spent on implementation and integrated quality assur-
How is the perceived software quality after implementation (including the
integrated quality assurance)?
3.2 Survey Design
In the survey design, we used and beneﬁted from lessons learned and guidelines
reported by other researchers in software engineering [
]. We therefore present
the sampling plan, the questionnaire design and the performed pilot test.
The sampling plan describes how the participants are repre-
sentatively selected from the target population. The ﬁrst decision, whether a
probabilistic, non-probabilistic or census sample should be considered, was al-
ready made by selecting the target audience. Given that no list of all companies
exists that have the characteristics of to target audience, a truly probabilistic or
census sample is not feasible. The ﬁrst (probabilistic) would require an enumera-
tion of all members of the target audience to select randomly participants and
the later (census) can as well only be conducted if all individuals of the target
audience are known. As a result, non-probabilistic sampling was chosen.
As a method to draw the sample from the population quota sampling with
the two strata geographical location of the software house (Germany, Austria
or Switzerland) and number of employees (less or equal 10, 11 to 100 and more
than 100) was applied.
Overall 57 software houses, 19 from each of the three countries, evenly dis-
tributed over the three company sizes were selected and could be consulted within
the given time and resources. Based on the activities relevant for software houses
from the OECD [
] industry categories, i.e., 62 – Computer programming, con-
sultancy and related activities, as well as 631 – Data processing, hosting and
related activities; web portals, the overall number of software houses in Germany,
Austria and Switzerland could be estimated based on data from governmental
Software Quality Assurance During Implementation 5
statistical oﬃces. For Germany, the “IKT-BRANCHE IN DEUTSCHLAND” [
report identiﬁed 61,029 companies in 2013 that are classiﬁed with one of the two
. For Austria, the governmental statistical oﬃce reported 13,281 com-
panies in the respective categories
in 2012. Finally for Switzerland, the federal
statistical oﬃce measured 2008 in the census of companies
15,466 companies that
have amongst their main activities programming, information technology consult-
ing and data processing. As a result, the total number of software houses in the
DACH region can be estimated with 90,000 (61
281 = 89
Taking the population size of 90,000 into account, with the 57 participating com-
panies a precision [
], which measures how close an estimate (resulting from the
survey data) is to the actual characteristic in the population, of 87% is achieved.
The questionnaire was designed based on the experi-
ences of Software Quality Lab and the involved researchers in conducting surveys
as well as academic ﬁndings of related surveys (see Section 2) and the software
engineering body of knowledge (SWEBOK) [
]. The knowledge and practical
consultancy experience of Software Quality Lab was a valuable input to design
the questionnaire. Furthermore, a technical report of a survey on software quality
conducted by Winter et al. [
] in 2011 provided many useful insights for the
questionnaire design. The questions included in the questionnaire were trans-
formed into closed-ended questions and ordered by topic. The questionnaire was
implemented and performed online with the survey tool LimeSurvey
. For soft-
ware quality assurance during implementation ﬁve questions were raised, i.e., one
question for RQ 1, one for RQ 2, two for RQ 3, and one for RQ 4, and embedded
into a larger questionnaire on software quality processes. The questions of the
questionnaire correspond to the research questions, where RQ 3 is split into two
questions, one for the development and one for the bug ﬁxing eﬀort. The answer
options for each of the ﬁve questions are shown in Fig. 2, Fig. 4, Fig. 6, Fig. 7,
and Fig. 8, respectively. The complete questionnaire is available via the ﬁrst
author upon request.
The questionnaire was validated internally, i.e., by the involved
researchers and Software Quality Lab, as well as externally by six employees of
software houses. Internally, there were several iterations and the involvement of
researchers and industrialists guaranteed a high quality review from diﬀerent
perspectives. Externally, the reviewers provided valuable, written feedback to
further improve the questionnaire.
3.3 Survey Distribution
The distribution of the questionnaires among the potential participants included
a pre-notiﬁcation, the invitation with the questionnaire, reminders and a thank-
you letter. The survey distribution started on April 1, 2015. The participants were
6 Felderer and Auer
selected by using Google Maps and searching for ‘software company’. Searching
for this term reveals all software companies at the related location. Furthermore,
information about the number of employees for each found software house were
determined. This allowed to come up with 450 participants – 50 small, 50 medium
and 50 big software houses per country. Two weeks after the pre-notiﬁcation
emails were sent, the invitation emails with a link to the online survey were
distributed. As a result, 13 participants responded to not wish to participate and
20 software houses participated. One reminder were sent in the middle of the
survey (end of April 2015) to remember possible participants about the survey.
Due to the low number of responses, additional 500 software companies were
contacted via email. In addition, new participants were searched and contacted
exclusively by phone to invite them to the survey. During three days within the
last week, 200 potential software houses in Germany, Austria and Switzerland
were called and asked for participation. In this three days 18 software houses
could be convinced to participate. Thus, the response rate for the phone calls
was 9%, which is double the response rate of the email invitations (4% for the
ﬁrst half of the survey and 3% for the second). In the phone calls, also some of
the reasons against the participation were mentioned. Amongst others, no time,
no interest, already having participated in similar surveys and the absence of
the respective decision maker were mentioned. The survey distribution ended on
May 22, 2015.
3.4 Survey Analysis
The data was ﬁrst analyzed quantitatively and then qualitatively by interviews
with survey participants and evidence extracted from related work.
As the responses for each question were nominally scaled, the votes for each
question were counted and then visualized in bar charts. Furthermore, Pearson
correlation coeﬃcients between answers were computed to ﬁnd correlations within
and between quality assurance techniques and tools to support implementation.
The analysis was performed in IBM SPSS and the resulting correlation coeﬃcients
have been interpreted as suggest by Evans [
], i.e., in steps of 0.2 from very
weak to very strong.
We performed 12 interviews with survey participants (i.e., 21% of all partici-
pants) to triangulate the quantitative analysis and to identify the reasons behind
some survey answers. The semi-structured interview type was chosen, because the
structured interview limits the discussion freedom to enter unforeseen subtopics
or ask questions that may arise during the interview. Another alternative would
have been the unstructured interview. However, this form would have not allowed
to ask prepared questions of interest that emerged during the analysis of the em-
pirical survey. Telephone calls were used contact each participant in an economic
and for the interviewee time- and place-ﬂexible way. In the short interview one
question on implementation was asked. In addition, the non-structured part of
the interview followed subtopics of interest that were raised by the interviewee or
that turned out as a result of previously conducted interviews to be of interest.
Software Quality Assurance During Implementation 7
3.5 Survey Timeline
This section summarizes the survey design, distribution and analysis by providing
the concrete timeline in which the respective activities were performed in 2015.
Figure 1 shows the timeline for survey design, distribution and analysis activities.
Activities with concrete dates in parentheses were performed in the given date
range, the other activities were performed during the whole month.
February •– collect requirements for the instrument
– setup and customize the online survey instrument
– explicit target audience characterization
March •– design sampling plan
– collect possible participants
– design and reﬁne questionnaire
– distribute pilot test invitations (30.3 - 31.3)
April •– distribute survey announcement emails (1.4 - 7.4)
– perform pilot test (8.4 - 16.4)
– evaluate and consider feedback, adapt questionnaire (17.4 - 21.4)
– perform survey (21.4 - 4.5); (27.4) send reminder
May •– extend the survey because of low participation (5.5 - 22.5)
– analyze the results
– describe and summarize ﬁndings
June •– analyze the results (22.5 - 7.6)
– prepare email invitations and interviews (13.6 - 14.6)
– send out email invitations for interviews (15.6)
– conduct interviews (16.6 - 19.6)
– analyze interview results (19.6 - 28.6)
– describe and summarize ﬁndings
Fig. 1. Timeline of the survey.
4 Results and Discussion
In this section, we ﬁrst present the demographics of our survey, then we present
and discuss main ﬁndings for each of the four research questions, and ﬁnally we
discuss threats to validity.
8 Felderer and Auer
Overall 57 software houses, 19 from Germany, 19 from Austria and 19 from
Switzerland, participated in the survey. Most of the software houses (84%) stated
that they perform more than one type of software project. On average three
types were stated. The three most common project types are development of
web-applications (71%), individual software (61%), and standard software (56%).
In the sample of 57 software houses small, medium and large companies are
present with a similar frequency: 38% of the companies are small-sized (up to 10
employees), 35% medium-sized (11 to 100) and 26% large-sized (more than 100
4.2 Main Findings
In this section we present the main ﬁndings for each of the four research questions.
RQ1: Quality Assurance Techniques
Figure 2 shows the quality assurance
techniques applied by the responding software houses during implementation. The
most commonly used techniques are unit testing (68%), code reviews (63%) and
coding guidelines (61%). Only one participants responded to apply no technique
0% 20% 40% 60% 80%
Quality assurance techniques during development. One participant responded
to apply no techniques at all.
So similar to Vonken et al. [
], we found a high ratio of people using cod-
ing guidelines. Furthermore, our results are also compliant with the ﬁnding of
Schindler  that pair programming is only applied moderately.
Software Quality Assurance During Implementation 9
Correlation analysis shown in Figure 3 revealed six positive relationships
between quality assurance techniques, i.e., using one technique increases the
likelihood of using the related one. Static code analysis is related to coding
guidelines, test-driven development (TDD) as well as unit testing. Furthermore,
coding guidelines are related to TDD, unit testing to code reviews and model-
driven development to behavior-driven development (BDD). Considering the
relationships, it is surprising that static code analysis is not amongst the most
commonly used techniques given its positive correlations to coding guidelines and
unit testing which are both used by more than 60% of the responding software
houses. Another notable, relationship is between model-driven development and
BDD that have only a signiﬁcant positive correlation to each other, but not to
Static Code Analysis
Coding Guidelines Model-Driven Development
Positive correlations between quality assurance techniques. Note that for all
relationship n= 57, which is why it is not explicitly mentioned at every correlation.
Quality assurance techniques like unit testing, code reviews or the usage of
coding guidelines are commonly practices according to the results of the empirical
survey. In addition, reviews, tests during development, checklists and reviews
at milestones are commonly used methods to control and support the software
development process. Thus, it seems that implementation is well supported by
respective quality assurance techniques. However, in the interviews with most
participants the domain-speciﬁc side of the software, the rules and peculiarities
of the domain, are seldom addressed explicitly, although they often lead to costly
RQ2: Tool Support
The participants were asked to indicate in which areas
implementation is supported by tools. The results depicted in Figure 4 show
that bug tracking (84%), version control (73%) and project management (63%)
are the most common areas. Continuous integration (42%) and requirements
management (38%) are mentioned only half as often as the other tool types.
10 Felderer and Auer
0% 20% 40% 60% 80% 100%
Fig. 4. Areas in which tools are used to support implementation.
Furthermore, relationships to quality assurance techniques were statistically
analyzed. The calculation of each possible correlation revealed four signiﬁcant
Coding guidelines are in a positive, moderate strong correlation with the use
of continuous integration (
000) and version control tools
(r= 0.426, p = 0.001).
Static code analysis is in a positive and moderate strong correlation with con-
tinuous integration (
000) and in a positive, weak relationship
with version control. In addition, it also has a positive, weak relationship with
bug tracking (r= 0.331, p = 0.012).
Code Reviews are in a weak, positive correlation with version control (
0.287, p = 0.030) and bug tracking (r= 0.367, p = 0.005).
Unit testing is in a weak, positive correlation with project management (
048) and in a strong, positive with bug tracking (
Thus, coding guidelines and static code analysis are often used in environments
with continuous integration and version control. Bug tracking tools are often
used in environments in which also static code analysis, code reviews and unit
testing are performed.
Correlations between the tool types have also been analyzed and are shown in
Figure 5: continuous integration tools are positively correlated with requirements
management and version control tools, and bug tracking tools are positively
correlated with version control and project management tools. One can observe
that all tools are related directly or indirectly (via other tools). Thus, it seems
that the introduction of one tool leads to the adoption of several others. This is
supported by the fact, that a high number of participants (64%) stated to use
three or more tools. Tools supporting implementation are therefore often used
Software Quality Assurance During Implementation 11
Positive correlations between the tool types. Note that for all relationships hold
that n= 57.
Also several other surveys ([
]) found that diﬀerent types of tools
are commonly-used to support development. Our results conﬁrm the ﬁndings of
Perez et al. [
] who also found that version control and bug tracking tools are
often used, but continuous integration only moderately. Furthermore, Vonken et
] also found high usage rates of version control systems. Finally, Schindler [
reported a similar usage rate of continuous integration (around 40%) as we did.
We asked for the ratio of the total project eﬀort spent on im-
plementation and integrated quality assurance. Figure 6 indicates a clear trend
towards the range 41% to 60% of the total project eﬀort, which was selected
by 49% of all participants. Thus, it seems that in practice most often a ratio
between 41% and 60% of the total project eﬀort is spent on implementation.
0% 10% 20% 30% 40% 50% 60%
Fig. 6. Amount of total project eﬀort dedicated to implementation.
12 Felderer and Auer
So in our case, the eﬀort spent on programming is higher than reported in
Garousi et al. [
] (i.e., 31%) and compliant with the ﬁnding of Armbrust et al. [
who reports a ratio of the overall eﬀort spent on implementation of 48% (and
integrated quality assurance).
The most important integrated quality assurance technique during implemen-
tation is bug ﬁxing. Figure 7 shows that with respect to the ratio of the total
project eﬀort developers spend on bug ﬁxing, most participants (63%) responded
that up to 20% of the total project eﬀort is spent on bug ﬁxing. Even higher
eﬀorts were stated by 25% of the respondents. Moreover, 12% could not estimate
the eﬀort for bug ﬁxing, which may indicate that they are not aware of the eﬀort
that is used for bug ﬁxing or do not measure it.
0% 10% 20% 30% 40% 50% 60% 70%
Fig. 7. Ratio of total project eﬀort spent on bug ﬁxing by developers.
According to two interviewees, a reason for the relatively high bug ﬁxing eﬀort
is that a small amount of bugs requires a large amount of time and eﬀort to be
ﬁxed. An example stated by one interviewee was a wrong variable name caused
by copying similar source code. Another reason that was commonly mentioned
are incomplete requirements speciﬁcations that result in bugs that are not caused
by wrong code, but by missing edge cases that were not properly speciﬁed. One
interviewee explicitly highlighted that not technical aspects cause high bug ﬁxing
eﬀorts, but domain aspects. Developers typically do not have the same deep
understanding of the business domain of the software as for instance requirements
engineers or customers have. This often results in bugs caused by missing or
wrongly interpreted domain-speciﬁc aspects of requirements.
Given that more developers work on the same code, bugs may be introduced by
other employees that have to ﬁx them. As a result, it could be that a higher ratio
of the total eﬀort is spent on bug ﬁxing with an increasing number of employees.
This is also supported by the fact that with an increasing number of developers
also the system complexity increases, which makes it more diﬃcult to ﬁnd and ﬁx
bugs. The statistical analysis conﬁrmed this correlation and identiﬁed a strong
positive correlation between the number of employees in software development
Software Quality Assurance During Implementation 13
and the eﬀort for bug ﬁxing (
000). Thus, the more
developers a software company has, the more eﬀort is spent on bug ﬁxing.
RQ4: Perceived Quality
We asked for the perceived software quality after
implementation (including the integrated quality assurance) at the handover
to system testing. Figure 8 shows that 41% responded to perceive the quality
neither as good nor as bad, 56%, more than half of all companies, rated the
quality as rather good to good. Only one participant mentioned that the quality
of the software at this point in development is rather bad. Thus, it seems that
the quality of the software is considered to be in an at least rather good quality
in most cases. This may indicate that the applied quality assurance measures
during the development are working.
0% 10% 20% 30% 40% 50%
Perceived software quality after implementation with integrated quality assur-
The software quality in development may also depend on the number of
developers. Thus, the correlation between the number of employees in total and
the number of employees in software development, respectively, as well as the
perceived software quality before testing was statistically analyzed. As a result,
two weak, negative correlations were found:
Perceived software quality is in a weak, negative correlation with the number
of employees (r=−0.334, n = 55, p = 0.013).
Perceived software quality is in a weak, negative correlation with the number
of employees in software development (r=−0.307, n = 55, p = 0.023).
These two negative correlations indicate that the higher the number of em-
ployees, the lower the perceived software quality is.
14 Felderer and Auer
4.3 Threats to Validity
In this section we discuss critical threats to validity and how we addressed them.
One critical threat to validity is the limited number of participating software
houses, i.e., 57. Nevertheless by estimating the overall number of software houses
in the DACH region, a precision of 87% of our results could be reached. To further
increase validity of the results, the questionnaire was triangulated by interviews
and evidence from related studies. Conclusions on correlation were drawn based
on statistical signiﬁcance.
Furthermore, it has to be mentioned that the study was only conducted in
the DACH region and that the validity of the results is therefore limited to that
region. But due to the facts that similar surveys were initially performed in the
DACH region and then replicated in other regions with similar results [
that we could not ﬁnd signiﬁcant diﬀerences between the results from Germany,
Austria and Switzerland, we think that the survey will deliver similar results in
other regions as well. However, a replication in other regions, which is already
planned as future work, is required to conﬁrm this statement. The questionnaire
itself was constructed based on the experiences of Software Quality Lab and the
involved researchers in conducting surveys as well as academic ﬁndings of related
surveys. Furthermore, the questionnaire was reﬁned by internal and external
reviews in several iterations.
This paper presented a survey on quality assurance during implementation in
software houses from Germany, Austria and Switzerland. Overall 57 software
houses participated. Results from the survey show that the most common software
quality assurance techniques used during implementation are unit testing, code
reviews and coding guidelines. Most tool support is used in the areas of bug
tracking, version control and project management. Due to relationships between
the used tool types, it seems that the introduction of one tool leads to the adoption
of several others. We also found that coding guidelines and static code analysis are
often used in environments with continuous integration and version control. Bug
tracking tools are often used in environments in which also static code analysis,
code reviews and unit testing are performed. Bug ﬁxing takes a signiﬁcant ratio of
the overall project eﬀort assigned to implementation. Furthermore, we found that
the more developers a software company has, the more eﬀort is spent on bug ﬁxing.
Finally, more than half of all companies rated the quality after implementation
as rather good to good and there seems to be a negative correlation between the
number of employees and the perceived software quality.
In future, we plan to replicate the survey in other regions and to perform case
studies to investigate in which context (for instance, with respect to the process
model applied) speciﬁc quality assurance techniques during implementation are
promising. Based on the results of these empirical studies we plan to derive
practical guidelines to improve quality assurance during implementation.
Software Quality Assurance During Implementation 15
The authors thank Software Quality Lab GmbH and especially its CEO Johannes
Bergsmann for joint operation of this survey as well as all participating companies,
interview partners and colleagues who helped to make this survey possible.
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