Yazılım Endüstrisi ve Akademi arasındaki Boşluğu Doldurmaya
Yönelik bir Anket Çalışması: İlk Sonuçlar
Deniz AKDUR [0000-0001-8966-2649] ASELSAN A.Ş., Ankara denizakdur@aselsan.com.tr
Özet. Akademik geçmişleri sadece "Bilgisayar-tabanlı Disiplinleri" değil, aynı zamanda Elektrik ve Elektronik Mühendisliği gibi “Bilgisayar-tabanlı olmayan” disiplinleri de kapsayan birçok mühendislik mezunu, üniversitede öğrendikleri yeteneklerin yazılım endüstrisinin beklentileri ile tam uyuşmaması sebebiyle mesleki kariyerlerine başladıktan sonra bir çok zorlukla karşılaşabilmektedir. Şirketler, endüstri için “hazır olmayan” ve farklı bir çok yazılım mühendisliği rolünde çalışacak bu personeli eğitmek için çok önemli kaynaklar harcamakta; bu yüzden akademi, endüstride en çok kullanılan ve ihtiyaç duyulan yetenekleri bilip buna uygun bir müfredat belirlemek zorundadır (hem kavramlar ve bilgi alanları gibi teknik yetenekleri, ing. hard skills hem de iş yerinde çokça ihtiyaç duyulan beşeri yetenekleri ing. soft skills). Bir diğer taraftan, "yazılımın geliştirilmesi, işletilmesi ve bakımı için sistematik, disiplinli ve ölçülebilir bir yaklaşım" olarak tanımlanan ve hem akademinin hem de yazılım endüstrisinin ortak çıktısı olan disiplinler arası bir uygulama olarak tanımlanan yazılım mühendisliğini şekillendirip kullanan bu akademisyen ve pratisyenlerin amaçları, katkıları ve kaygıları çok farklı olabilmektedir. Tüm bunlar düşünüldüğünde, hem yazılım endüstrisinin yazılım mühendisliği ile ilgili olabilecek akademik programlardaki müfredatların ihtiyaçlara göre belirlenebilmesi; hem de endüstri profesyonellerinin akademik dünyaya bakış açısını yansıtarak daha fazla endüstri-akademi işbirliği (EAİ) sağlanabilmesi için bir anket çalışması hazırlandı. Değişik yazılım mühendisliği rollerindeki (örneğin, yazılım geliştiricisinden, sistem ve test mühendisine kadar) farklı sektör ve uygulama alanlarından, 14 ülkeden 637 katılımcıya ulaşan bu anketle, endüstrinin akademiden beklentileri ve ihtiyaçları adreslenerek “Yazılım endüstrisi akademiden ne istiyor?” sorusu cevaplanmaya çalışılmıştır. Bu bildiride anket sonrası ön analiz sonuçları sunularak, hem akademi hem de endüstride farkındalık yaratılıp, müfredatlarnın şekillenmesi, güncellenmesi ve de “akademiden daha hazır” gelebilecek profesyoneller sayesinde şirketlerin eğitim, oryantasyon gibi maliyetlerinin azaltılması hedeflenmektedir.
Anahtar Kelimeler: yazılım endüstrisi, yazılım mühendisliği eğitimi, bilgisayar-tabanlı disiplinler, müfredat, anket, endüstri akademi işbirlikleri (EAİ)
A Survey on Bridging the Gap between Software Industry and
Academia: Preliminary Results
Deniz AKDUR [0000-0001-8966-2649] ASELSAN A.Ş., Ankara denizakdur@aselsan.com.tr
Abstract. Many engineering graduates, whose academic backgrounds are not only based on "Computing Disciplines" but also non-computing ones, face difficulties after beginning their professional careers in the software industry due to misalignment of the skills learnt in the
university with what is required in the workplace. Companies spend crucial resources to train these personnel, who are not “ready” for different software engineering (SE) roles. Academia must know what skills (e.g., hard skills such as key SE topics and knowledge areas (KAs) besides soft skills) are mostly used to adapt the educational programs via an effective curriculum. Although SE is shaped and used by both practitioners and academicians, these two separate worlds have different goals and concerns. In order to increase Industry-Academia Collaboration (IAC) in SE, it is necessary to understand different perceptions of practitioners about academia. To achieve these objectives, an online survey, which explores the gap between the software industry expectations and academic activities, was designed and conducted. The survey was responded by 637 software practitioners from 14 countries working in different SE roles with different highest academic degrees, work experiences, application types and industrial sectors. In this article, the preliminary analysis on survey results are presented.
Keywords: software industry, software engineering education, computing discipline, non-computing, curriculum, practitioner survey, industry academia collaborations (IAC)
1
Introduction
Software practitioners in the industry face difficulties to align the skills learnt in the university with what is required in the workplace. Since Software Engineering (SE) is defined as “the appli-cation of the systematic, disciplined and quantifiable approach to the development, operation and maintenance of software” [1], this definition affects how or to whom related topics are taught in the university; hence most resesearch focuses on improving the curriculum for “Computing Disci-plines” (e.g., Computer Science (CS), Computer Engineering (CENG), Information Technology (IT) and Information Systems (IS)). However, the practitioners have a variety of different SE roles (e.g., from software developers to systems engineers and testers), whose academic backgrounds are not only based on these computing disciplines but also non-computing ones such as Electrical and Electronics Engineering Engineering (EEE). It is reported that practitioners in the embedded software industry, who were graduated from any non-computing disciplines are lacking knowledge in key SE topics and knowledge areas (KAs), which they learn or improve themselves during the job (e.g., after university education) [2, 3]. In our previous survey, which investigated software modeling practices in embedded sectors (e.g., consumer electronics, defense & aerospace), ~40% of respondents were graduated from non-computing disciplines, which shows that there is a huge number such practitioners [4]. Therefore, the need for an effective curriculum for different SE roles should not focus on only computing disciplines but also a wider perspective. It is crucial to analyze the gap between the software industry expectations and the academic curriculum for two reasons: (1) for software industry, it is important to hire properly trained (e.g., “ready”) graduates, which allows them to spend less time while incorporating these personnel more efficiently into the workplace; (2) for academia, understanding the necessary skill set is critical for curriculum maintenance and development.
SE, which is the application of engineering to software [1], is shaped and used by both acade-micians and practitioners. However, these two different worlds have totally different goals, contri-butions and concerns. Since academicians and practitioners have different mindsets (e.g., motiva-tion, focus, etc.), the level of Industry-Academia Collaboration (IAC) in SE is low and they are not collaborating with each other to solve industrial problems. Depending on demographic factors such as educational-skill set, SE role or previous poor experience on IACs, there are mismatches in perceptions of various practitioners. It is very necessary to investigate different perceptions of practitioners about academicians in order to increase IACs by improving mutual understanding of each side.
In order to address the need to align different SE roles’ education with software industry and to understand different opinions of the practitioners about the academic world, a practitioner survey was designed and conducted. 637 software practitioners from 14 different countries participated in this survey, which closes the gap between industry and academia. The participants have a variety of different SE roles from software developers to systems engineers and from testers to high level managers, whose academic backgrounds are based on both computing disciplines (CS, CENG, IS, etc.) and non-computing ones (especially EEE from embedded software industry). In this article, the preliminary analysis on survey results (mainly demographics data) are presented besides
inter-esting findings, which will be planned to analyze various cross-factor analysis for different de-mographics to increase IACs in SE.
The remainder of this paper is structured as follows. Section 2 gives the related studies. Section 3 presents the research methodology. Section 4, which is the main goal of this article, presents the preliminary results. Finally, Section 5 concludes this study by giving future directions.
2
Related Studies
In the literature, the knowledge gap between the industry needs and educational programmes was highlighted by several studies (e.g., [5-10])). When analysing necessary SE skills, there are two primary concepts: “hard skills” and “soft skills”. Hard skills can be seen as “technical” skills that are gained in the academic curriculum. On the other hand, although soft skills can also be im-proved in these curricula, there is almost no dedicated course for these topics. Rather, students are expected to get these skills through in-class activities or during team projects. Many studies fo-cused on these skills and their results show that both hard and soft skills are needed in order to understand the needs of the software industry.
This survey study builds on previous studies with significant extensions: it is not limited to ei-ther the educational background (e.g., computing disciplines), nor a subset of SE roles (e.g., not only developers or IT personnel), nor just a specific region (e.g., not only USA, UK, Finland, New Zealand), nor having too general results to address specific needs (e.g., focuses on application type such as embedded vs desktop or industrial sector (e.g., Consumer Electronics, Defense & Aero-space, IT & Telecommunications)). In this perspective, this survey, by including practitioners’ opinions about academia, closes the gap between industry and academia with a wider coverage.
3
Research Methodology
In this study, the online survey method was chosen to obtain information from various practition-ers in a quick manner to analyze these data easily [11].
The main goal of this survey is to bridge the gap between software industry expectations and academic activities. This main goal is decomposed into three sub-goals: (1) Identifying the usage and importance of SE KAs and topics by measuring knowledge gaps with the industry needs and academic curriculum (e.g. “hard skills” analysis); (2) Understanding the most important soft skills; (3) Analysing the opinions of practitioners about IAC on both educational and research activity sides. Based on these goals, by creating corresponding survey sections, the following research questions (RQs) are raised:
RQ1: What are the most used KAs and SE topics in the software industry? What are the knowledge gaps and coverage of the industry expectations after university education?
RQ2: How does educational skill set of the practitioner affect software modeling approach and practices? RQ3: What are the most important soft skills in the industry?
RQ4: What are the opinions of software practitioners for more IAC as a part of the education?
RQ5: How do practitioners see academicians? What are their perceptions about academicians and aca-demic outputs?
In order to develop a survey that would adequately cover the goal of this study, the organization of survey was carefully designed by considering survey guidelines [11] and also previous experi-ence in designing & executing industrial surveys (e.g., [4]). For this study, due to space con-straints, please refer to [12] for the details of all design phases activities of the survey, which took over six months (e.g., the selection of critical SE topics and identifying the practitioners’ opinions about how they see academics, expert opinion, survey piloting, etc.).
The organization of the survey is depicted in Fig. 1. After gathering the demographics of the participants, the survey questionnaire consisted of five main sections [13], whose preliminary re-sults are presented in Section 4. To design and execute the survey, the Google Forms tool was used. The ethics approval for the survey was issued by the Human Subjects Ethics Committee of Middle East Technical University (METU) in March 2019. The survey was then executed in the period of March-May 2019.
Fig. 1. The organization of the survey
4
Preliminary Analysis
Opinions of 637 software practitioners with different demographics from 14 different countries are included in the survey. In this section, due to space constraints, the demographics of participants and the –limited- preliminary results of RQ1 (without cross-factor analysis) are presented. Please refer to [14] for the other RQs’ raw data results.
4.1 Demographic of participants and their companies
It is necessary to get a detailed demographics in such a study since practitioners’ and projects’ characteristics directly affect the results. The first question asked respondent about the country that the participant is working. The final dataset had respondents from 14 different countries as depict-ed in Fig. 2. Note that due to researcher’s location (i.e., Turkey), the ratio of Turkey (56%) is higher than others.
Participants were asked to provide their highest academic degrees. The result reveals that ~57% of participants have a postgraduate degree (MSc 38.6% and PhD 18.4%) and 43% of re-spondents have a BSc degree as shown in Fig. 3.
Fig. 3. Highest academic degrees
In order to understand the respondents’ educational skill-set, participants were then asked to provide their university degrees. The participants, who answered the highest academic degree question as “BSc”, respond only the question for BSc university degree; however participants, who have any postgraduate degree (e.g., either MSc and/or PhD) present their both university de-grees (i.e., BSc and postgraduate dede-grees respectively). Fig. 4 depicts BSc degrees of participants, which shows that EEE graduates, which is one of the non-computing disciplines, have a significant percentage (24%) in the participant pool.
Fig. 4. BSc degrees
The need for an effective curriculum for different SE roles should not focus on only computing disciplines but also a wider perspective; hence it is better to categorize the university degrees ac-cording to the “computing discipline” criteria by taking reference on Table 1 information.
Table 1. Computing disciplines vs Non-computing disciplines for university degree
Computing Disciplines Non-computing Disciplines
Computer Engineering (CENG) Electrical and Electronics Engineering (EEE)
Computer Science (CS) Telecommunication Engineering (TE)
Information Systems (IS) Electronics and Communication Engineering (ECommE) Software Engineering (SE) Industrial Engineering (IE)
Electrical and Computer Engineering (ECE) Mathematics (Math) Information Technology (IT) Systems Engineering (SysE)
Accordingly, when this new “derived” criteria is used, the results showed that 25% of partici-pants (i.e., 159 participartici-pants) took non-computing disciplines curriculum for BSc although they are working in the software industry (Fig. 5).
As given in Fig. 3, ~57 % of participants (i.e., 363 software practitioners) have a postgraduate degree and whenever this type of university degrees are analysed, Fig. 6 is depicted.
Fig. 6. Postgraduate (MSc and/or PhD) degrees
In the final pool of participants (e.g., after BSc and postgraduate degree (if any)), when the percentage of computing vs non-computing disciplines is analysed, it is seen that the percentage of the participants, who took any computing disciplines curriculum increased about 6,3% (i.e., 518 participants, 81.3%) as depicted in Fig. 7. This might mean that these non-computing disciplines took any computing discipline postgraduate degree to improve themselves in the industry.
Fig. 7. Computing vs non-computing disciplines in the final participant pool
SE practices used in the industry and opinions of practitioners about IAC do not only depend on their university education (e.g., not only based on the courses taken in BSc or postgraduate) but also depend on the experiences and training undertaken during the job. In other words, we cannot isolate the gained skill set and perceptions of the practitioner from the workplace, which might also affect the results of such a study. Therefore, besides university degree(s) (e.g., computing vs non-computing), it is also very necessary to know the relations between the countries, where the practitioner has worked and the country, where this practitioner completed his university education (e.g., BSc and postgraduate, if any). Note that a practitioner, who completed his/her BSc degree in one country, might go abroad for postgraduate education or for work during their professional career; hence knowing the –possible- differences between these countries is crucial to compare the results. Therefore, after getting university degree for BSc and postgraduate (if any), the coun-try/countries, where the practitioner completed their university education are asked as another questions. The goal of these questions is to create a map, which shows the educational and career path of the software practitioner after BSc education. Fig. 8 shows such information, where you can trace the path of the participants, who go abroad for any postgraduate education or profession-al career or stay the country, where she/he completed her/his BSc education.
Fi g . 8 . Th e p at h o f th e p arti cip an ts a fter u n iv ersity e d u ca ti o n (Th e co u n tr y /co u n tri es, w h ere th e p arti cip an ts co m p lete d u n iv ersity d eg re e(s) an d w h ere , th ey a re c u rre n tly w o rk in g in )
Apart from these information, knowing SE role(s) in their careers, the work experience (e.g., for the perceptions of experienced professionals vs fresh graduates), the type of applications (e.g., embedded vs desktop vs web) and industrial sector (e.g., consumer electronics, defense & aero-space, automotive, etc.) are also important factors to better understand and align the gap between software industry and academia. To achieve this goal, participants were asked about these de-mographics data in multiple response questions.
Most of the participants have “Software Developer/Programmer” role in their career (91.2 %) as depicted in Fig. 9. “Software Architect” is the other majority role, which is mostly achieved after being “Software Developer/Programmer” for a while (e.g., after ~5-10 years of work experi-ence). In other words, since this question is multiple response question, multiple roles could be recorded (e.g., a person can be a software developer/programmer at the beginning of its profes-sional career and then become a software architect). Similarly, a software practitioner, who is cur-rently working as a “Middle / High Level Manager”, might be a software developer at the very
beginning of her/his career, then become a software architect, and perhaps after 15+ years of work experience, become a software manager. Individual responses and the results of this question also help to understand possible career path for the software practitioner [14].
Fig. 9. SE roles in the professional career
According to the results, the majority (43%) had 10-15 years of work experience, followed by 5-9 years (32.8%) and 15+ years (17.7%) of work experience as depicted in Fig. 10.
Fig. 10. Work experience
Participants were also asked about the type of the applications developed as a multiple-response question. “Embedded applications” is the majority (63 %), which is followed by “Desk-top applications” and “Web applications” (Fig. 11). Some participants (i.e., ~1 %) used “free text” area for this question as “Other”. After analyzing these responses, some of them are merged into the existing categories (e.g., “mobile” is counted as “embedded”; or “server” is counted as “web”).
Fig. 11. Application type
When the target sectors of the products developed by the company employing the participants was asked, the results are depicted in Fig. 12. Three most popular industrial sectors are “Defense & Aerospace”, followed by “Consumer Electronics”, and “IT & Telecommunications”.
4.2 Preliminary Results
In this section, due to space constraints, RQ1 of this study, which investigates the most used KAs and SE topics by exploring the knowledge gaps in the software industry is addressed as prelimi-nary analysis without any cross-factor.
In order to align different SE roles' education (from software developer to systems and test engineers) with industry expectations, Section 1 of the survey aims at identifying the most im-portant hard skills in the industry by shedding light on usage/importance and knowledge gaps of different SE topics. After identifying the necessary KAs and SE topics based on curriculum guide-lines such as SWEBOK and ACM Computing Curricula ([1, 15]) and by reviewing similar surveys (See Section II) and the improvement with personal industrial experience, Section 1 mainly asks whether the participant took a specific course related to this item (e.g., either a KA or a SE topic). Then, by asking the usage of this item (i.e., as Likert scale from Never(0) to Always(5)), it is aimed to analyze the knowledge gaps to address RQ1.
As depicted in Fig. 13, as expected almost every participants (98,5%) took a course related to “Software Construction”, which is directly related with programming and coding. 63,1% of partic-ipants took a specific course related to “Software/Systems Design and Architecture”, followed by “Software Quality” (45%) and “Software Engineering Management” (42%).
Fig. 13. KAs taken / not taken in the university education
According to the results, the most used KA – as expected again - is “Software Construction”, as seen from Fig. 14. However, there are some interesting findings, when analyzing the knowledge gaps in the industry. Although some KA related courses are not taken in the university education; their usage (hence the importance level) are very high, which causes knowledge gaps in the soft-ware industry. According to the results, “Softsoft-ware Configuration Management”, which was not taken any related course by 88,4% of participants, almost always (e.g., 4,416 level of 5) is used in the industry. This significant gap is the greatest gap among the other KAs.
Fig. 14. Usage/Importance of KAs
Similarly, “Software/Systems Analysis & Requirement Engineering”, which 65,5% of partici-pants did not take a course dedicated to this KA, is mosty used (e.g., 4,336 level of 5) in the indus-0% 1indus-0% 2indus-0% 3indus-0% 4indus-0% 5indus-0% 6indus-0% 7indus-0% 8indus-0% 9indus-0% 10indus-0% Software Engineering Economics
Software Configuration Management Software Testing Software/Systems Analysis & Requirement…
Software Engineering Management Software Quality Software/Systems Design & Architecture Software Construction (Programming & Coding)
Taken in the university Not taken in the university / I do not know
0,943 3,020 3,133 3,823 4,173 4,336 4,416 4,518 0 1 2 3 4 5
Software Engineering Economics Software Engineering Management Software Testing Software Quality Software/Systems Design & Architecture Software/Systems Analysis & Requirement…
Software Configuration Management Software Construction (Programming & Coding)
try as depicted in Fig. 14. “Software Testing” is also another knowledge gap, which the academia
should give more importance since the given usage/importance are not correlated with what the university curriculum include. On the other hand, “Software Engineering Economics”, which was not taken as a course by many participants (94,8%) is the least used KA, which means that im-portance of this KA is less than the others. In this case, since its usage is also low (i.e., 0,943 level of 5) as seen from Fig. 14, there is no significant knowledge gap for this KA.
After understanding the knowledge gaps for specific KAs, the participants were asked about different SE topics in a similar manner. The most popular courses (e.g., the curriculum include these courses so that most participants took them) are “Data Structures” (90,6%), “Operating Sys-tems” (89,8%) and “Hardware concepts & Computer Architecture” (88,1%). On the other hand, only 1,4% of participant took a course related to “Blockchain fundamentals” as seen Fig. 15.
Fig. 15. SE topics taken / not taken in the university education
After analyzing the usage of these SE topics, Fig. 16 is depicted. Accordingly, “Data struc-tures”, “Object-oriented (OO) concepts” and “Database & File systems” are the top three used SE topics in the software industry.It is seen that there is no big gap for a specific SE topics as opposed to knowledge gaps encountered in KAs. In other words, the usage/importance of SE topics are correlated with the ratio for the courses taken in the university.
Fig. 16. Usage/Importance of SE topics
0% 20% 40% 60% 80% 100%
Blockchain fundamentals Business Intelligence & DSS Cloud computing (Cyber)Security & Cryptography Web services AI & Machine Learning Embedded Systems & Software Graphics & Imaging & Visualisation Database & File systems Algorithms and Complexity Object-oriented (OO) concepts Networking & Data communication Hardware concepts & Computer architecture Operating Systems Data structures
Taken in the university Not taken in the university/ I do not know
0,688 1,052 1,298 1,449 1,571 1,750 2,192 2,338 2,359 2,647 2,719 3,061 3,214 3,339 3,845 0 1 2 3 4 5 Blockchain fundamentals Business Intelligence & DSS (Cyber)Security & Cryptography AI & Machine Learning Cloud computing Graphics & Imaging & Visualisation Web services Hardware concepts & Computer architecture Networking & Data communication Embedded Systems & Software Operating Systems Algorithms and Complexity Database & File systems Object-oriented (OO) concepts Data structures
Mathematical Foundations helps software engineers to comprehend various SE related topics, which in turn is translated into programming language code or logic rather than on challenging arithmetic abilities. Note that instead of asking this KA in the previous section, which addresses various KAs such as Software Construction and Software Quality (Fig. 13 and Fig. 14), we want-ed to understand which specific mathematic courses are uswant-ed hence important for different SE roles in which application types. Therefore, in a specific sub-section, the participants were asked about the usage/importance of five mathematical-related courses after asking their existence in their curriculum. As seen Fig. 17, all participants (100%) took “Calculus” and “Probability & Statistics” in the university; whereas 94% of participants took “Differential Equations”, 86,5% of participants took “Propositional/Predicate Logic”, and 72,6% of participants took “Discrete & Combinatorial Mathematics”. On the other hand, the most used mathematical-related course is “Probability & Statistics” (i.e., 1,876 level of 5), whereas “Differential Equations” is the least used topic (i.e., 0,397 level of 5). As seen, there is a big gap (e.g., negative gap) in “Differential Equa-tions” since this course is almost taken by everyone (i.e., 94%) although its usage is very low in the software industry. As a big picture, no mathematical related courses’ usage is higher than 2 level of 5, which might mean that there is a misalignment of what is needed in the industry and given in the university. Mathematics should be included in curricula, but perhaps the weight of some mathematics-related courses (e.g, the given course hours) might be updated.
Usage/Importance of Mathematics-related courses
Fig. 17. Mathematical Foundations KA
Note that all the preliminary results given in this section are raw data taken from the survey without filtering any participants’ demographics. However all the usage/importance of these KAs and SE topics will be more helpful when cross-factor analysis (e.g., the most important skill set according to application type(s) such as embedded vs web; or industrial sector such as defense or consumer electronics) , which has been already started for different demographics are identified.
5
Conclusion and Future Directions
In this article, due to space constraints, the preliminary results of a survey, which aims to bridge the gap between the software industry expectations and academic activities, was presented.
With the help of the collected data, whose cross-factor analysis for different demographics (both educational and professional life) have been already started, academia can adapt their pro-grams with an effective curriculum for different SE roles (e.g., from software developers to testers and systems engineers) in different sectors so that various graduates will be more efficiently incor-porated into the software industry. Moreover, revealing practitioners’ perceptions about academia and academic activities with demographic differences (e.g., regional, application type, sector, SE roles, etc.) will, hopefully, increase IACs in SE.
0% 50% 100%
Discrete & Combinatorial Math. (Propositional/Predicate) Logic Differential Equations Probability & Statistics Calculus
Taken in the university
Not taken in the university / I do not know
0,397 1,220 1,273 1,730 1,876 0 1 2 3 4 5 Differential Equations Discrete & Combinatorial…
Calculus (Propositional/Predicate) Logic Probability & Statistics
Acknowledgments. The author would like to thank all software practitioners, who contributed to
this survey.
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