Resume-Driven Development: A Definition andEmpirical Characterization

Jonas Fritzsch, Marvin Wyrich, Justus Bogner, Stefan WagnerUniversity of Stuttgart, Germany, Institute of Software Engineering

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Abstract—Technologies play an important role in the hiringprocess for software professionals. Within this process, severalstudies revealed misconceptions and bad practices which lead tosuboptimal recruitment experiences. In the same context, grey lit-erature anecdotally coined the term Resume-Driven Development(RDD), a phenomenon describing the overemphasis of trendingtechnologies in both job offerings and resumes as an interac-tion between employers and applicants. While RDD has beensporadically mentioned in books and online discussions, thereare so far no scientific studies on the topic, despite its potentialnegative consequences. We therefore empirically investigated thisphenomenon by surveying 591 software professionals in bothhiring (130) and technical (558) roles and identified RDD facets insubstantial parts of our sample: 60% of our hiring professionalsagreed that trends influence their job offerings, while 82% of oursoftware professionals believed that using trending technologiesin their daily work makes them more attractive for prospectiveemployers. Grounded in the survey results, we conceptualizea theory to frame and explain Resume-Driven Development.Finally, we discuss influencing factors and consequences andpropose a definition of the term. Our contribution providesa foundation for future research and raises awareness for apotentially systemic trend that may broadly affect the softwareindustry.

Index Terms—software development, technology, hiring, careerdevelopment, survey, theory

I. INTRODUCTION

“Specific technologies over working solutions, hiring buz-zwords over proven track records, creative job titles over tech-nical experience, and reacting to trends over more pragmaticoptions” [1] – this is the future of software development ifwe would adhere to this satirical manifesto for Resume-DrivenDevelopment. According to its signatories, the phenomenon isalready present today. This invites the question if there is morethan anecdotal evidence to it.

Ebert and Counsell state in their projection of the state ofSoftware Technology in the year 2050 that “Software-drivensystems are exhibiting rapidly growing complexity [...] basedon a fast-changing technology landscape” [2]. They furtherproject that “overwhelming complexity, combined with insuf-ficient development competences, greatly decreases software-driven products’ quality” which would become evident in e.g.“public’s decreasing acceptance of [...] self-driving cars” [2].Already a decade ago, Adomavicius et al. regarded the sheernumber of available technologies and the complex set ofrelationships among them as a major challenge [3].

The recent Stack Overflow 2020 Developer Survey among65.000 software developers found that “around 75% of respon-

dents [...] learn a new technology at least every few months oronce a year” [4]. The survey also revealed that technologiesare regarded as the most important job factor by softwareprofessionals. This focus may come at the expense of otherskills, leading to “insufficient development competences” [2]as Ebert and Counsell state it.

In the context of the software professional recruiting pro-cess, lively discussions have come up in developer forums,blogs, or social media where occasionally the terms Resume-Driven Development (RDD) [5], [6], [7] and similarly CV-Driven Development [8] were used. Classon associates this no-tion with selecting “tech stacks, architecture, methodologies,and protocols based on what looks good on the resume” [9],while Ford et al. describe it as a pitfall by architects becomingenamored in latest technologies [10]: “utilizing every frame-work and library possible to tout that knowledge on a resume”.The topic tends to provoke heated and even polemic debates,as e.g. visible in [11] or [12].

While the term RDD has been sporadically used in booksand online discussions, we have not found any empiricalinvestigation of the phenomenon nor a definition and theoryof the term in scientific literature. Thus, we see a need forfurther investigation and clarification. Software developmentprofessionals and decision makers will equally benefit froma more in-depth understanding of a phenomenon that maybe systemic in nature and has the potential to substantiallyaffect software development practice. In this study, we identifystakeholders, their intentions and relationships, as well as otherinfluencing factors constituting to the phenomenon Resume-Driven Development. As a result, we provide a definition andtheoretical construct to substantiate this notion. Consequently,we formulate our research objective as follows:

Conceptualize a theory for Resume-Driven DevelopmentFor the purpose of framing and explaining the phenomenonWith respect to its scope, influences, and potential impactFrom the viewpoint of human resource, software professionals,and computer science studentsIn the context of the software industry

To refine our objective and to set the scope for a more detailedinvestigation, we formulate several research questions. In thiscontext, we distinguish between a hiring and an applicantperspective. While software professionals sometimes act inboth roles, hiring professionals and computer science studentsare commonly associated exclusively with one perspective.

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RQ1: How valuable does hiring perceive technology-relatedcharacteristics of an applicant’s skill set, i.e. “breadthvs. depth” and “established vs. latest”?

RQ2: To what degree is hiring influenced by latest / trendingtechnologies when creating job offerings?

RQ3: To what degree are applicants influenced by latest /trending technologies when refining their resumes?

RQ4: What experiences did applicants have in practice whenusing latest / trending technologies?

To answer these research questions, we designed and con-ducted an exploratory survey among software professionalswith both hiring and technical roles as well as computerscience students. A total of 591 participants could be recruited.Based on the statistical analysis of the survey results, we de-veloped a theory for analyzing and describing Resume-DrivenDevelopment (RDD) following the framework for theories ininformation systems proposed by Gregor [13].

II. BACKGROUND AND RELATED WORK

In this section, we define the problem area and present existinganecdotal evidence, which leads to a vague understanding ofRDD. We characterize the involved stakeholders and put ourresearch into the context of similar phenomena and relatedwork.

A. Problem Area and Scope

We first define stakeholders and their relationships as objectsof investigation. As an abstraction, we declare stakeholdersrepresenting a company or employer as the hiring perspective.Usually, this responsibility lies with people managers, teamleads, hiring professionals, or head hunters in the softwareindustry. On the opposite side, we define software profes-sionals (developers and related job roles) as employees inthe applicant perspective. Computer science students are alsocovered by this perspective. For the relationship between bothperspectives within the recruiting process, the following arti-facts are of particular importance: job advertisements issuedby hiring and resumes or curricula vitae (CVs) submittedby applicants. The desired / promoted skills with regards totechnologies play an important role for job advertisements andresumes in software development [4].

Ideally, the technologies in a job advertisement reflect thecompany’s actual demands. Likewise, we would expect appli-cants to promote their actual skills and experiences in theirCVs. Potential problems arise if expectations or assumptionsof the other perspective consciously or unconsciously influencethe creation of these documents. Improperly prioritized oradvertised information could lead to misunderstandings in theshort term and more severe consequences in the long term.

To investigate how technology selection is influenced bythese forces, we distinguish between established technologiesand latest / trending technologies. Instances of the latter(also referred to as hyped technologies) can be frameworks,programming languages, middleware, concepts, or architec-tural styles which have recently gained excessive industry

attention, e.g. through print / online media, conferences, socialmedia, video platforms, or blogs. Some concrete examplesare microservices, IoT, DevOps, Docker, Serverless, ApacheKafka, Go, Kotlin, Kubernetes, Angular, Vue.js, TensorFlow,or Blockchain technologies. In contrast, established technolo-gies are of a more conservative nature, widely accepted sinceyears, and a large body of knowledge exists on how to usethem correctly.

B. Related Phenomena and Studies

We distinguish the meaning behind Resume-Driven Develop-ment from the phenomenon of Hype-Driven Development [14],[15], [16], which refers to a similar concept but on a moregeneral level. There, trending technology is not seen as aninfluence to the job market, but for alleged product opti-mizations, e.g. to proactively plan ahead. Another relatedphenomenon is Cargo Cult Software Engineering [17]. It ischaracterized by the blind use of code, design patterns, ortechnologies without fully understanding the reasons behindit. Here, the motivation to adopt a technology or practice isbased on having seen its successful application somewhereelse.

In recent years, several studies have revealed flaws in thehiring process of tech companies. These studies examine howthe skills of potential candidates are tested and which biasesare involved. In particular, such studies address wrong as-sumptions about what interviewers expect [10], psychologicalaspects of solving coding challenges [18], [19], or the impactof individual characteristics in this context [20]. The resultscast reasonable doubts on the ability of current interviewpractices to fairly find suitable candidates for building diversesoftware teams [21].

The recent Stack Overflow Developer Survey 2020 showedthat languages, frameworks, and other technologies [4] are byfar the most important job factor for developers. Accordingto Gant [22], developers tend to “fixate on programming lan-guages and technology stacks as measurements of their value”,although it would be possible to “build a perfectly acceptableapplication using tools that aren’t popular”. More than one-third of the 65,000 Stack Overflow’s survey participants learna new technology every few months and another third atleast once a year. A complementary study by Montandon etal. [23] on more than 20,000 Stack Overflow job postingsalso confirmed the importance of this aspect for the hiringperspective. They found expertise in specific programminglanguages and frameworks to be the most required hard skillby companies.

While existing studies show that developers frequently learnnew technologies and that specific technologies are one ofthe most frequent requests in job advertisements, a systematicstudy that links these concepts and examines their interactionis still missing. We do not yet know in which ways companiesand software developers are influenced by technology trendsand if developers learn new technologies to stay competitiveor mostly out of pleasure and self-motivation. With our study,we aim to fill these gaps and to shed light on the influence of

latest and trending technologies from both the hiring and theapplicant perspective.

III. RESEARCH DESIGN

In this study, we conducted basic research which aims toprovide understanding rather than a solution to the problem[24]. We applied inductive logic, also referred to as a bottom-up approach to theory-building [25]. Starting from relatedliterature, we collected background information and insightsabout the problem area. After identifying stakeholders andrelationships among them, we designed and conducted an ex-ploratory survey to gain insights for both perspectives. We col-lected quantitative data via a questionnaire and used statisticalmethods for the analysis. To ensure scientific rigor, we followthe seven-step guideline for survey design by Kasunic [26],complemented by Linaker et al.’s more recent annotations [27].Finally, we derived general conclusions resulting in a theoryabout the phenomenon RDD.

A. Sampling Strategy

Our target population is determined by the above definedperspectives, namely practitioners and students in a softwaredevelopment context with hiring or technical roles. For dis-tributing the survey, we relied on convenience (or accidental)sampling which is a common approach in software engineer-ing [27]. Because such a sample may not be representative,we used several distribution channels in parallel to reach alarger and more diverse audience [28]. We distributed thesurvey via a leading German online magazine for softwaredevelopers (Heise), professional platforms (LinkedIn, XING),social networks (Twitter), and developer forums (Reddit).Moreover, we contacted about 100 IT and software companieswhich participate in a recurring career fair at our university.Using mailing lists, we also reached out to enrolled studentsof several computer science courses of our and a neighboringuniversity. Lastly, personal contacts of all involved researcherswere invited to participate. Participants were also encouragedto forward the survey to their professional contacts in thesoftware industry.

B. Questionnaire Design

To cover both perspectives, we designed a twofold web-basedsurvey. Participants were able to choose their perspective(hiring, applicant, or both) at the beginning. In the survey,we used the term technical for the applicant perspective tomake the selection clearer. For creating the questionnaire,we transformed each of the research questions into severalsurvey questions. We used mostly closed-ended descriptivequestions to ease analyzability. Because we were interestedin attitudes and opinions, the majority of questions reliedon a 5-point Likert scale [29] to measure agreement (itemsranged from “strongly disagree” to “strongly agree”). Ad-ditional demographic questions referred to aspects like jobrole, years of professional experience, or company domain.All non-free-text questions were mandatory. However, forsensitive questions, we provided a “Prefer not to answer”

option. Answering the survey took approximately 5 minutes.The complete questionnaire was in English and we used theEvaSys SurveyGrid application [30] to create and host it.We ensured anonymity to all participants and consequentlyremoved all personal information from the published data set.

C. Pilot Test and Data Collection

Four members of our research group reviewed the ques-tionnaire, which led to several refinements in wording andlayout. To test the analysis procedure, the questionnaire waspopulated with random data in a simulation run. The resultswere then used to pilot the analysis script, which again led toimprovements for both the script and the questionnaire. Thefinal survey was activated on May 14th, 2020 with two monthsof data collection until July 14th, 2020. To minimize the riskof outages of the hosting service, we sent out the invitationsstaggered over several days. We regularly monitored responserates and reminded personal contacts after two to four weeks.As an additional incentive, we pledged to donate e1.00 toUNICEF for each of the first 100 responses, thereby support-ing children’s rights to an education.

D. Statistical Analysis and Theory Building

After retrieving the results, the first step was data cleaning.We removed incomplete responses, incomplete answers (e.g.for the ranking questions), unnecessary columns, and personalinformation from the final comment field. As a result, weobtained 591 valid responses out of 593. In addition to datatype and missing value harmonization, we also resolved thefree-text answers into consistent values (e.g. country or “other”options). With the final data set, we then first used descriptivestatistics and Likert plots to analyze the distributions. For in-ferential statistics, we started with an exploratory analysis via acorrelation matrix (Spearman). These results were then used tobuild and continuously refine linear regression models. Lastly,the models were successfully tested for required propertieslike the absence of heteroskedasticity (Breusch-Pagan test withp >> 0.05).

For our theory, we relied on the taxonomy proposed byGregor [13]. Her work provides a seminal framework fortheory building. In our study, we conceptualized a theory foranalyzing, also called Type I theory. Such theories “describeor classify specific dimensions or characteristics of individualgroups, situations, or events by summarizing the commonali-ties found in discrete observations. They state ‘what is’” [13].Descriptive theories are needed when nothing or very little isknown about the phenomenon in question. Compared to highertheory types, they do not emphasize causal explanations. Dueto the unexplored nature of RDD, we focus on the descriptionof observed characteristics and relationships between them.Future studies will be needed to support our theory.

IV. SURVEY RESULTS

A wide range of hiring and software professionals participatedin our survey. In total, we received 593 responses of which591 were valid. Due to the various distribution channels, we

can only provide an estimate for the response rate based onour regular monitoring. For contacts approached via personalemail invitations or distribution lists, we saw timely responsesfor 5 to 10% of the addressees. Of the 591 valid responses, 130answered for the hiring and 558 for the applicant perspective,i.e. 97 participants answered for both perspectives.

TABLE I: Responses per Perspective and Role

Role # Hiring # ApplicantSoftware Engineer 193People Manager / Team Lead 115 27CS Student 102Architect 76Consultant 36Researcher / Lecturer 33System Administrator 29DevOps Engineer 27Project Manager 20Head Hunter 8Test / Quality Engineer 8Requirements Engineer 7HR Professional 7Overall 130 558

TABLE II: Professional Experience of Respondents

# Years of Experience # RespondentsNone 26 (4.4%)2 or less 93 (15.7%)3-5 131 (22.2%)6-10 103 (17.4%)10-20 135 (22.8%)21 or more 103 (17.4%)Overall 591 (100%)

TABLE III: Industry Affiliation of Respondents

Company Domain # RespondentsSoftware & IT Services 246 (41.6%)Automotive 84 (14.2%)Manufacturing & Defense 38 (6.4%)Research & Education 37 (6.3%)Finance & Insurance 31 (5.2%)Public Sector 21 (3.6%)Healthcare 19 (3.2%)Retail 13 (2.2%)Hardware & Semiconductors 12 (2.0%)Logistics & Public Transport 12 (2.0%)Telecommunications 10 (1.7%)Energy & Utilities 9 (1.5%)Recruitment 8 (1.4%)Media & Advertising 7 (1.2%)Construction & Real Estate 3 (0.5%)None 41 (6.9%)Overall 591 (100%)

TABLE IV: Company Size of Industry-Affiliated Respondents

# Employees in Company # Respondents1-25 87 (15.8%)26-100 87 (15.8%)101-1,000 143 (26.0%)1.001-10,000 76 (13.8%)>10,000 157 (28.5%)Overall 550 (100%)

Table I shows the number of participants per role and per-spective. For hiring, People Manager / Team Lead was thedominant job role with 115 out of 130 responses. For theapplicant perspective, the majority of respondents filled therole of software engineer or architect, with 102 responsesbeing from students of various computer science study pro-grams. As indicated by Table II, the majority of respondentswere experienced IT professionals, ∼58% reported more than5 years of professional experience.

Concerning the respondents’ company domains (see Ta-ble III), more than 40% worked for a Software & IT Servicescompany. The remaining portion, however, is spread widelyover 14 domains, with Automotive, Manufacturing & Defense,Research & Education, and Finance & Insurance being repre-sented with more than 30 mentions each. The reported com-pany sizes show a similar diversity (see Table IV). Very smallbusinesses (below 100 employees) are equally represented aslarge enterprises with more than 10.000 employees, with bothgroups in the region of 30%. As expected, the vast majority ofparticipants stated Germany as their country of residence (529,i.e. ∼90%). 42 respondents were located in other Europeancountries and 20 on other continents.

A. Hiring Perspective

In this section, we present the results for the hiring perspective,which correspond to our research questions 1 and 2. ForRQ1, we were interested in how valuable hiring perceivesthe characteristics “breadth vs. depth” regarding an applicant’sskill set. We asked for the importance of the following twocharacteristics while assessing applicants:

1) broad knowledge and experience in numerous technolo-gies

2) deep knowledge and experience in specific technologiesRespondents assigned fairly equal weights, with ∼73% an-swering “agree” or “strongly agree” for broad, while ∼66%did the same for deep (see Fig. 2). However, when askedwhich of the two types would generally be better suited forthe their team or company, the results were different. Forcedto decide for one or the other, ∼42% now preferred applicantswith broad knowledge, while only ∼22% chose applicants withdeep specific knowledge. ∼36% selected the neutral option,stating that this could not be generalized or that they had noopinion on the matter (see Fig. 1).

In the second part of RQ1, we inquired after the perceivedimportance of experience with latest / trending vs. establishedtechnologies in an applicant’s skill set. We again asked forthe importance of the following two characteristics during anapplicant assessment:

1) knowledge and experience in latest / trending technolo-gies

2) knowledge and experience in established technologiesThe vast majority (∼85%) confirmed knowledge in establishedtechnologies to be important (“agree” or “strongly agree”). Forlatest / trending technologies, this fraction was considerablylower, but still ∼59% (see Fig. 2).

We again forced the direct comparison by selecting the moresuitable characteristic for their team / company: Here only20% would prefer the applicant experienced in latest / trendingtechnologies, while ∼39% chose experience with establishedtechnologies. However, a similar percentage (∼41%) remainedneutral or had no opinion (see Fig. 1).

39%39%20% 41%41%knowledge in latest / trending technologiesno opinion / cannot be generalized knowledge in established technologies

42%42%22% 36%36%

Response

deep knowledge no opinion / cannot be generalized broad knowledge

Which type of applicant is generally better suited for your team / company?

Fig. 1: Preferred Skills by Hiring Professionals

For RQ2, we analyzed to which degree hiring is influencedby latest / trending technologies when creating job offerings.A majority of ∼59% (“agree” or “strongly agree”) confirmedthat technology trends and hypes indeed impact the tech-nologies advertised in their job offerings (see Fig. 2). Aneven larger majority (∼71%) believed that applicants favorworking with the latest / trending technologies. When askedfurther, if expectations of potential applicants would have aninfluence on technologies advertised in their job offerings,∼46% confirmed it (“agree” or “strongly agree”), while ∼25%answered contrarily (“disagree” or “strongly disagree”).

In a last question, we asked hiring participants if it is easyfor their company or customer to find qualified employees forsoftware development positions. ∼59% answered “disagree”or “strongly disagree”, while only ∼15% answered “agree” or“strongly agree” (see Fig. 2), i.e. many of our respondentsperceived it as difficult to fill their vacancies.

B. Applicant Perspective

In this section, we present the results for the applicantperspective, that correspond to RQ3 and RQ4. We wereinterested, to which degree applicants are influenced by latest/ trending technologies when optimizing their resume andhow they perceive hiring attractiveness in this context (RQ3).Additionally, we wanted to learn about applicants’ practicalexperiences in using latest / trending technologies (RQ4).

When asked, how attractive it is to work with latest / trend-ing technologies, the majority agreed that they enjoy usingthem in their daily work (∼73%). Simultaneously, only ∼18%perceived it as inconvenient or stressful to regularly learnnew technologies (∼22% neutral, ∼60% disagree). However,the belief that using latest / trending technologies in theirdaily work would make them better software developers wasconfirmed by only ∼42% (see Fig. 2).

Complementary to this intrinsic motivation, respondentswere also largely convinced that using latest / trending tech-nologies in their daily work makes them more attractive for po-tential future employers: ∼82% answered “agree” or “stronglyagree”, while only ∼5% stated “disagree” or “strongly dis-agree”. Not quite as unanimous but still confirmed by ∼63%was the belief that learning a diverse set of latest / trending

technologies would be even more desirable for potential futureemployers, i.e. “the more hyped technologies I learn, the moreattractive I become” (see Fig. 2).

In the last question group, we asked for practical experi-ences with technology selection and usage. Nearly half of ourparticipants (∼49%) stated that their past experiences withusing latest / trending technologies in products and projectswere mostly positive, while ∼19% disagreed and ∼32% votedneutral (see Fig. 2). Moreover, ∼20% reported that they had atsome point knowingly selected latest / trending technologiesalthough those had not been ideal for the use case. We thenasked applicants to rank the importance of four influencingfactors for selecting a technology at the start of a new project.We calculated the following mean scores from the responses,ranging from 1 (least important) to 4 (most important):

a) Functional and non-functional system requirements (3.54)b) Skill sets of current employees (3.06)c) Latest / trending technologies (1.72)d) Skill sets of potential future applicants (1.69)

In this ranking, the most reasonable (requirements) and prag-matic (current skills) options were reported as most influential.Latest / trending technologies were ranked third, closelyfollowed by skills of future employees. Finally, participantswere asked if they believed that the regular emergence of newtechnologies increased technological diversity in their teams orcompanies: ∼67% confirmed this tendency, while only ∼11%disagreed and 22% voted neutral (see Fig. 2).

V. DISCUSSION

In this section, we holistically discuss and interpret thepresented survey results. For both perspectives (hiring andapplicant), we review the evidence that supports or refutesthe existence of RDD while listing influencing factors basedon correlation analysis and regression modeling.

Within the hiring perspective, we mainly analyzed twoaspects to judge the prevalence of RDD:

a) the degree to which technology trends and hypes influ-ence job offerings

b) the degree to which expectations of applicants influencejob offerings.

For a), almost 60% of our participants agreed that trends andhypes influence their job offerings. For b), the percentageagreement was lower but still half of our participants admittedthat applicant expectations influence their job offerings (46%).Simultaneously, 71% believe that applicants favor workingwith latest technologies and 49% that applicants are discour-aged by legacy technology. However, our participants alsoclearly showed a tendency to judge experience with establishedtechnologies as overall more important. In a direct comparison,only 20% would generally hire the applicant versed in latest/ trending technologies. Moreover, hiring generally preferredbroad experience with numerous technologies over deep ex-perience with specific technologies, which would be in linewith the notion of continuously broadening your skills withe.g. new languages or frameworks.

5%

6%

73%

66%

22%

28%

broad knowledge and experience in numerous technologies.

deep knowledge and experience in specific technologies.

Response

Hiring Perspective I: When looking for new employees, it is important for me that applicants have ... (N=130)

0%

14%

85%

59%

15%

27%knowledge and experience in latest / trending technologies.

knowledge and experience in established technologies.

100 50 0 50 100

Percentage

Response

8%

19%

18%

25%

59%

71%

59%

49%

46%

15%

22%

22%

32%

28%

26%It is easy for my company to find qualified employees forsoftware development.

Technology trends and hypes have an influence on thetechnologies that we advertise in our job offerings.

Expectations of potential applicants have an influence on thetechnologies that we advertise in our job offerings.

Applicants favor working with the latest / trendingtechnologies.

Applicants are discouraged by the prospect of working withestablished or legacy technologies and systems.

100 50 0 50 100

Percentage

Response strongly disagree (1) disagree (2) neutral (3) agree (4) strongly agree (5)

Hiring Perspective II: Influences on job offerings & assumptions about applicants (N=130)

5%

9%

13%

28%

60%

82%

73%

63%

43%

18%

13%

18%

23%

29%

22%

I enjoy using the latest / trending technologies in mydaily work.

Using the latest / trending technologies in my daily workmakes me a better software developer.

I perceive it as inconvenient or stressful to regularlylearn new technologies.

Using latest / trending technologies in my daily work makesme more attractive for potential future employers.

The more latest / trending technologies I learn, the moreattractive I will be for potential future employers.

100 50 0 50 100

Percentage

Response strongly disagree (1) disagree (2) neutral (3) agree (4) strongly agree (5)

Applicant Perspective I: Attitude towards latest / trending technologies (N=558)

11%

19%

37%

60%

67%

49%

43%

20%

22%

32%

20%

19%

My team can freely select the technologies for its projects /products.

In the past, my team used latest / trending technologiesalthough we knew that this was not the most suitable choice.

In the past, we made mostly positive experiences with usingthe latest / trending technologies in our products / projects.

The regular emergence of new technologies increases thetechnological diversity in our team / company.

100 50 0 50 100

Percentage

Response strongly disagree (1) disagree (2) neutral (3) agree (4) strongly agree (5)

Applicant Perspective II: Experiences with latest / trending technologies (N=558)

Fig. 2: Survey Results by Question Groups for Hiring and Applicant Perspectives

The combination of the evidence from a) and b) suggests theoccurrence of RDD facets in substantial parts of our hiringsample: even though experience with established technologiesis generally preferred, there are strong indications that jobofferings are influenced by technology trends and applicantexpectations. To identify influencing factors, we built a linearregression model that tries to predict a combined constructfrom a) and b), namely the mean value of the influence oftrends and hypes on job offerings and the influence of appli-cant expectations on job offerings. Predictors for this modelwere the belief that applicants favor latest technologies andthe preference for broad technological experience in applicants(see Fig. 3). For every additional point on one of these Likertscales, the RDD hiring construct (also normalized to a 5-point

Likert scale) increases by 0.36 and 0.17 points respectively.However, we did not find any additional significant predictorsin our hiring sample. No intuitive candidate like the companysize, the company perception (from conservative to innova-tive), or the perceived ease to fill vacancies could be verified.While the overall model has a p-value of 1.553× 10−5, itis only able to explain less than 20% of the variability ofthe construct (adjusted R2 = 0.159), i.e. there are otherinfluencing factors we did not capture.For the applicant perspective, the occurrence of RDD wasmainly judged by two aspects:

a) the degree to which applicants believe that experiencewith latest / trending technologies makes them moreattractive for employers

b) the degree to which trends and hypes influence technol-ogy selection

In contrast to the expressed preference of our hiring profes-sionals for experience with established technologies, the vastmajority of our applicant participants believed to be morehireable via trends and hypes: 82% agreed that using latest/ trending technologies in their daily work makes them moreattractive for employers and 63% even agreed that the more ofthese technologies they learn the more attractive they become.This suggests substantial RDD effects that incentivize to keepup-to-date with trends and hypes, especially when consideringthat only 43% believe that using latest / trending technologiesin their daily work actually makes them better developers.Considering aspect b), however, the influence of both systemrequirements (3.54) and the skill sets of current employees(3.06) were ranked considerably higher than latest / trendingtechnologies (1.72). Simultaneously, just 20% confirmed tohave used hyped technologies in an unsuitable way, althoughonly 49% stated that they made mostly positive experienceswith such technologies.

β = 0.17, t(126) = 2.21, p = 0.029

β = 0.36, t(126) = 4.76, p = 5.14e−06

β = − 0.03, t(126) = − 0.38, p = 0.705Perceived hiring ease

"Broad experience with numerous technologies is important."

"Applicants favor working with latest / trending technologies."

0.0 0.2 0.4Regression coefficient β

Factors Influencing the RDD Hiring Construct

AIC = 292, BIC = 307Fig. 3: Factors Influencing the RDD Hiring Construct (Linear Re-gression Model)

In combination, a) and b) suggest that RDD facets are sim-ilarly prevalent in our applicant sample as in our hiringsample. However, the strong evidence from a) and weakerevidence from b) may mean that applicant notions of RDDare frequently latent without actually being acted out duringprofessional technology selection. One reason for this couldbe that only 43% of our participants agreed that they canfreely select technologies in their projects. When buildinga similar linear regression model (see Fig. 4) to predict acombined RDD applicant construct (the mean value of thedegree to which applicants believe to become more hireablevia using latest technologies in their daily work and theinfluence of hypes on technology selection), we identified theenjoyment of using latest / trending technologies (β = 0.19)and the belief to become a better developer by using latest /trending technologies (β = 0.12) as the top predictors. Smallerinfluencing factors were positive experiences with trendingtechnologies, the belief that trends and hypes lead to moretechnological diversity, and the company size.

The intuitive notion that RDD would be more prevalent withmore inexperienced developers could not be fully verified inour sample: while years of professional experience showedsome weak correlations with RDD applicant facets like the

β = − 0.03, t(453) = − 1.68, p = 0.094

β = 0.05, t(453) = 2.68, p = 0.008

β = 0.19, t(453) = 5.42, p = 9.93e−08

β = 0.12, t(453) = 4.24, p = 2.75e−05

β = 0.07, t(453) = 2.49, p = 0.013

β = 0.08, t(453) = 2.51, p = 0.012

Years of professional experience

Company size (# of employees)

"Latest / trending technologies lead to more technological diversity."

Positive experiences with latest / trending technologies

"Using latest / trending technologies makes me a better developer."

"I enjoy working with latest / trending technologies."

0.0 0.1 0.2Regression coefficient β

Factors Influencing the RDD Technical Construct

AIC = 695, BIC = 728Fig. 4: Factors Influencing the RDD Applicant Construct (LinearRegression Model)

belief that using latest technologies daily increases attractive-ness for employers (rs = −0.198, p = 3.8× 10−6), it was nota significant predictor when considering other influences in ourlinear model (p = 0.094). Likewise, being a student had nosignificant impact. The model has a p-value of 2.2× 10−16,but is only able to explain roughly one third of the variabilityof the construct (adjusted R2 = 0.311).

VI. A THEORY OF RESUME-DRIVEN DEVELOPMENT

Our results suggest that RDD facets exist in substantial partsof our population. Using the framework from Gregor [13],we propose an analytic theory (Type I) to describe the phe-nomenon. We use text and a diagram for its representation(see Fig. 5). Central constructs are the two perspectives,shaped by their characterizing attributes, and the influencingfactors that impact the strength of RDD. As such, the RDDconstruct comprises the hiring and the applicant perspectives.An influencing factor strengthens (+) the construct of oneperspective. The four aspects with a regression coefficientgreater than 0.1 are tagged with “++”. The probability thatpractitioners exert facets of RDD increases with seven aspects(see Fig. 5), two for the hiring and five for the applicantperspective.

The main point expressed by our theory is the technologyfocus of both parties when creating job advertisements andresumes respectively. We can observe a tendency by hiringto cater to the preferences of applicants, who mostly enjoyusing new technologies. However, in reality, such manipulativeadvertisements are not fully in line with the actual demandof employers. Applicants, on the other hand, may react tothis technology focus even stronger, promoting them in theirresumes in turn. RDD may thus develop a momentum of itsown. While it seems intuitive to suspect the shortage of skilleddevelopers as a driver for RDD, we found no related predictors

to be significant in our sample (e.g. the perceived hiring ease).In summary, we define the phenomenon as follows:

Definition. Resume-Driven Development (RDD) is aninteraction between human resource and software profes-sionals in the software development recruiting process. Itis characterized by overemphasizing numerous trending orhyped technologies in both job advertisements and CVs,although experience with these technologies is actuallyperceived as less valuable on both sides. RDD has thepotential to develop a self-sustaining dynamic.

The scope of our theory has been described in Section II andis also subject to our limitations (see Section VIII). However,we are confident that our proposed theory at least partiallydescribes a prevalent trend in the software industry as a whole.

VII. POTENTIAL CONSEQUENCES OF RDDPotential consequences of RDD have to be considered in twomain areas. First, the described phenomenon may negativelyimpact software quality. Our participants widely agreed thatthe regular emergence of new technologies increases techno-logical diversity in their companies. While we did not askif they perceived this heterogeneity as negative, RDD seemsto contribute to the software complexity issue mentioned inthe introduction. A high degree of technological heterogeneitymay impact maintainability: technologies need to be regularlyupdated, dependencies have to be managed, and knowledgesharing efforts among team members increase with everyadditional language or framework. Moreover, introducing newtechnologies implies a learning curve and may come withmaturity issues that impact reliability. Extensive RDD-basedtechnology selection may therefore lead to complex or evenunmaintainable software consisting of technologies which arenot suitable for the requirements, which are unfamiliar tocurrent or future employees, or which did not deliver on theirpromise and were discontinued.

Second, RDD can lead to false expectations and disap-pointment in the recruiting process on both sides. In a recentHackerRank survey among 71,281 developers [32], the topanswer for “What turns developers off from employers?” withan affirmation of more than two-thirds was “not enough clarityon role or where I’ll be placed”. Similarly, Behroozi at al. [21]identified a number of suboptimal practices which may sabo-tage recruitment processes in a study of over 10,000 Glassdoorreviews. A prevalent theme among them were inadequatelycommunicated criteria by HR. RDD-based hiring can lead tosimilar frustrations. A strong focus on technologies duringhiring may also lead to the neglection of other important skillsfor creating high-quality software products in a team. This isreinforced by nine hiring professionals who provided free-text comments about applicant traits they value much morethan experience with trending technology, e.g. soft skills likecommunication, self-motivation, the willingness to learn, beinga cultural fit for the team, or an understanding of the funda-mental principles behind technologies. Since high employee

turnover can be especially harmful in knowledge-intensivefields like software development, potential consequences ofRDD hiring should not be underestimated.

VIII. THREATS TO VALIDITY

Limitations of our study have to be mentioned in severalareas. One threat to construct validity is the absence of similarworks or questionnaires we could build on. While we carefullyreviewed related literature, used proven Likert scales, andfollowed established guidelines, there is still a chance thatsome of our questions did not suitably capture what weactually wanted to measure.

Several precautions were taken to ensure internal validity.To mitigate consistency and representation issues, we pilotedand revised the questionnaire several times and also consultedother researchers. Some respondents may still have giveninaccurate or untrue answers, either deliberately, due to misun-derstandings, or due to cognitive bias. As mitigation strategies,we defined important terms at the start of the questionnaire.To create a common understanding of the involved concepts,our description of “latest / trending technologies” also in-cluded “hyped technologies”, which some may interpret as aderogatory term. Participation was also strictly voluntary andwe assured anonymity for further processing and publishingof the collected data. The English questionnaire could haveinfluenced non-native speakers, but we expect this to beminimal in the software engineering field. Lastly, we did notuse the term RDD to avoid bias in participants.

A threat to conclusion validity could arise from the ex-ploratory nature of our survey. As such, we did not haveformal hypotheses, e.g. for testing if RDD exists, but reliedon our interpretation of distributions. To minimize researcherbias, we discussed all important results between the first threeauthors until consensus was reached. A confounding factorfor answers to the applicant perspective could be prescribedtechnology choices by employers, as reported by 43% of ourparticipants. We may have missed other such factors for thephenomenon, which is also supported by the low degree ofvariance our regression models are able to explain. Moreover,while using linear regression instead of simple correlationanalysis improved our understanding of relationships in ourdata, we still cannot make statements about causality.

For scientific SE surveys, we had a high number of 558responses for the applicant and 130 for the hiring perspective,with diversity in work experience, company size, and domain.A threat to external validity, i.e. generalizability, may be thatthe majority of participants were located in Germany (∼90%).Regional and cultural factors could influence the phenomenonand results could partially differ in a sample dominated byparticipants from, e.g., the US. Furthermore, our applicantsample contained 102 students (18%). While we think thatthe views of students are also relevant for the applicantperspective, they may differ from the opinions of professionals.Nonetheless, we still believe the fundamentals of our theoryto be applicable to a broad range of software engineeringcontexts.

Hiring Perspective

“Tech trends and hypes have aninfluence on our job offerings”

“Expectations of potential applicantshave an influence on our job offerings”

Applicant Perspective

“Using latest / trending tech makesme more attractive for employers”

Importance of latest / trending techfor selecting a technology in a project

•

•

•

•

Résumé-DrivenDevelopment

Larger company size (# of employees)+“Using latest / trending tech. makes me a better developer”++

Positive experience with latest / trending technologies+“Latest / trending tech. lead to more technological diversity”+

“Applicants favor working with latest / trending technologies”++“Broad experience with numerous technologies is important”++

“I enjoy working with latest / trending technologies”++

Fig. 5: Theory of Resume-Driven Development. Human resource and software professionals take the perspectives of hiring andapplicant respectively, which are characterized by their facets in RDD. Strengthening predictors are linked to each perspective via +for β < 0.1 and ++ for β > 0.1 (see Fig. 3 and 4). Icons from [31].

Finally, our survey was conducted during the lock-downsrelated to COVID-19 in 2020, confronting human societywith significantly changed living and working conditions. Westill believe that responses were not seriously affected, sincerelated opinions should have formed over a longer timeframe.We also assume that the long-term economic impact will belimited and thus not drastically change the characteristics ofthe investigated phenomenon.

IX. CONCLUSION

In this study, we empirically characterized and defined thephenomenon Resume-Driven Development (RDD) by inter-preting results of a survey with 591 hiring and softwareprofessionals. The survey confirmed the occurrence of RDDfacets in substantial parts of our sample. Hiring professionalsvalued a broad technical skill set in applicants. The majorityof them agreed that applicants favor working with latest /trending technologies and that those technologies influencetheir job offerings. Simultaneously, they preferred experiencewith established technologies in applicants. The majority ofour applicants, on the other hand, believed that using latesttechnologies in their daily work makes them more attractivefor employers and still more than half believed that the moreof these technologies they learn the more attractive they be-come. However, less software professionals reported negativeexperiences with or unsuitable selection of such technologies.

Based on the results, we proposed a descriptive theory toscope and partially explain RDD. The theory contains a defi-nition, the characterization of the two RDD perspectives, andinfluencing factors that strengthen or weaken RDD. Potentialconsequences of RDD are mainly decreased software quality

and increased employee turnover due to false expectations onboth sides.

Future research needs to confirm or reject this theory,i.e. more evidence is needed to analyze the phenomenon.Replicating this survey, especially in different geographicaland cultural settings, may provide valuable insights to adaptand extend the theory, e.g. the influences and consequences.Furthermore, the rich results of qualitative interviews may shedmore light on practitioners’ thought processes and rationales.Overall, we think that an understanding of RDD, its preva-lence, and its consequences are important in a time where newtechnologies, frameworks, and languages are released moreand more frequently.

X. DATA AVAILABILITY

We openly share the questionnaire, anonymized raw data, andR script for the analysis.1

ACKNOWLEDGMENT

The authors would like to thank Maximilian Jager, Universityof Mannheim, for his consultation on the used statisticalmethods and Daniel Graziotin, University of Stuttgart, for hisfeedback on the questionnaire and paper.

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