The Product Backlog
Palo Alto, CA, USA
Carnegie Mellon University
Silicon Valley Campus
University of Auckland
Auckland, New Zealand
Carnegie Mellon University
Electrical and Computer Engineering
Silicon Valley Campus
Moffett Field, CA 94035, USA
Abstract—Context: One of the most common artifacts in
contemporary software projects is a product backlog comprising
user stories, bugs, chores or other work items. However, little
research has investigated how the backlog is generated or the
precise role it plays in a project.
Objective: The purpose of this paper is to determine what is
a product backlog, what is its role, and how does it emerge?
Method: Following Constructivist Grounded Theory, we con-
ducted a two-year, ﬁve-month participant-observation study of
eight software development projects at Pivotal, a large, interna-
tional software company. We interviewed 56 software engineers,
product designers, and product managers. We conducted a survey
of 27 product designers. We alternated between analysis and
theoretical sampling until achieving theoretical saturation.
Results: We observed 13 practices and 6 obstacles related to
product backlog generation.
Limitations: Grounded Theory does not support statistical
generalization. While the proposed theory of product backlogs
appears widely applicable, organizations with different software
development cultures may use different practices.
Conclusion: The product backlog is simultaneously a model of
work to be done and a boundary object that helps bridge the
gap between the processes of generating user stories and realizing
them in working code. It emerges from sensemaking (the team
making sense of the project context) and coevolution (a cognitive
process where the team simultaneously reﬁnes its understanding
of the problematic context and ﬂedgling solution concepts).
Index Terms—Product backlog, dual-track agile, scrum, lean,
extreme programming, user stories, design thinking, user-
centered design, feature engineering
Aproduct backlog (or simply backlog) is a list of work
items (e.g., user stories, outstanding bugs, various chores) used
by software teams to coordinate work to be done. Developers
typically build new features, modify existing features, and ﬁx
bugs based on the items at the top of the backlog. For many
teams, the backlog is the primary project management artifact
or primary non-code artifact. Whoever ﬁlls and prioritizes the
backlog directs the product’s development.
Much research has addressed the process of constructing
software systems, often based on backlog items. Scrum, Ex-
treme Programming, and Kanban all concern building working
software based on some kind of work item list or pool.
However, little empirical research has explored how backlog
items are generated, reﬁned, and organized over time. A
common but na¨
ıve response is that you just ask potential users
or clients what they want. This does not explain how backlog
items are generated; it just shifts the burden of designing the
system onto the user.
Similarly, little empirical research has investigated the roles
or functions of the backlog in contemporary software projects.
Again, a na¨
ıve response is to assume a backlog is some agile
analogue of a software requirements speciﬁcation. As we shall
see in Section III-C, a product backlog is not a requirements
While the concept of a backlog comes from Agile meth-
ods, the methods literature does not answer these questions.
Agile method(ologie)s tend to focus on: the organization of
development (e.g., sprints, team member roles); intra-team
communication (e.g., stand-up meetings, burndown charts);
and development practices (e.g., continuous integration, pair
programming). They tend to emphasize working closely with
users to co-design the product but do not directly address how
to determine what to build.
Pre-agile methods typically recommended “eliciting re-
quirements” from stakeholders (including potential users) and
then reﬁning them until the solution is deﬁned. This approach
can have two problems:
1) Users typically do not have system requirements. Users
have vague, unreliable, conﬂicting, evanescent prefer-
ences, values, beliefs, and guesses about what might
make a product successful , .
2) Design is a creative process—one must imagine a solu-
tion , . Pre-agile methods offer little guidance for
imagining good solutions.
The lack of existing research speciﬁcally investigating the
product backlog motivates the following research question:
Research Question: What are product backlogs, what
are their roles, and how do they emerge?
This article describes the research method (Section II),
summarizes the results (Section III), continues with a brief
discussion (Section IV), and overviews related work (Section
V). Section VI concludes the paper by summarizing its main
II. RESEARCH ME TH OD
We conducted a longitudinal study at Pivotal Software—
a large, international software company with twenty ofﬁces
around the world—over two years and ﬁve months. We used
Constructivist Grounded Theory , which involves iteratively
collecting and analyzing data to generate and reﬁne a theory.
We began by asking, “What is happening at Pivotal when
it comes to software development?” Data collection included
participant observation (that is, simultaneously observing and
participating in activities), interviews, questionnaires, and ex-
amining organizational documents and records. We analyzed
this data using constant comparison and several qualitative
analysis techniques, as described below.
Because this study is so large, multiple core categories
emerged, including sustainable software development ,
team code ownership , software development waste , and
the topic of this paper: the product backlog.
As the product backlog core category emerged, we incorpo-
rated data from a survey of the company’s product designers,
performed additional interviews, and continued participant
observation to reﬁne the category. We constantly compared
emerging ﬁndings to data from these three sources until
reaching saturation, as described below.
A. Research Context
We studied Pivotal because it is successful, open to re-
search, and interesting in its use and evolution of Extreme
Programming. More speciﬁcally, we studied Pivotal Labs: a
division of Pivotal that helps to transform clients’ engineering
cultures while building products. Client personnel work with
Pivotal personnel at a Pivotal ofﬁce where they can experience
Extreme Programming  in an environment conducive to
Typical teams include about six software engineers, one
product designer, and one product manager. In some smaller
teams, one person ﬁlls both the product designer and product
manager roles. Larger projects are organized into smaller
coordinating teams, each with one product manager and one
or two product designers.
Pivotal has practiced Extreme Programming  since the
late 1990s. While each team autonomously decides what is
best for each project, the company culture strongly suggests
following all of the core practices of Extreme Program-
ming, including pair programming, test-driven development,
refactoring, weekly retrospectives, daily stand-ups, prioritized
backlog, and team code ownership.
We observed eight greenﬁeld and brownﬁeld projects from
various domains, including automotive, networking, health-
care, publishing, e-commerce, virtual machine management,
and a multi-node database. Each project had one to three
product managers, zero to two product designers, and four
to 28 developers. Project details are available elsewhere .
B. Data Collection and Analysis
This paper analyses more diverse data than is typical in
grounded theory studies. Participant observation produced
voluminous ﬁeld notes, supported by internal documents and
code artifacts. Intensive interviews of 56 product designers,
product managers, and software engineers who had experience
with Pivotal’s software development process from ten different
ofﬁces helped us enter into participants’ perspectives. (“In-
tensive interviews” are “open-ended yet directed, shaped yet
emergent, and paced yet unrestricted” .) An informal online
questionnaire, with responses from 27 of 71 total product
designers (a 38% response rate), produced further insights into
The ﬁrst author iteratively collected and analyzed ﬁeld
notes and interviews; used line-by-line coding  to iden-
tify nuanced interactions in the data and avoid jumping to
conclusions; and reviewed the initial codes while reading the
transcripts and listening to the audio recordings. We discussed
the coding during weekly research collaboration meetings and
recorded insights from these discussions as grounded theory
memos . We stored initial codes in a spreadsheet and used
constant comparison to generate focused codes.
The ﬁrst author routinely compared new codes to existing
codes, reﬁning them and eventually generating categories. We
periodically audited each category for cohesion by comparing
its codes. When this became complex, we printed codes on
index cards to facilitate reorganization. We wrote memos to
capture the analysis of codes, examinations of theoretical
plausibility, and insights. Eventually, a small number of core
categories, including the product backlog, emerged. An online
appendix contains examples of categories and coding.1
We drew on other relevant documents, code artifacts, and
questionnaire results to elaborate and saturate categories. Since
line-by-line coding of such documents is intractable, we in-
stead wrote memos about them, as recommended by Glaser
[12, p. 98].
We continued theoretical sampling on all data sources
until no further backlog-related categories were evident (i.e.,
theoretical saturation). Theoretical saturation was not truly
achieved until midway through writing this paper, which
is normal for Grounded Theory studies [5, p. 285]. The
articulation of the results, below, was the key indicator that
saturation had been reached.
Data collection and analysis were continuous but not uni-
form (e.g., some interviews were too close for much inter-
vening analysis). Similarly, some weeks we did considerable
analysis; others very little. This is not ideal but is unavoidable
in a longitudinal inquiry where everyone has conﬂicting com-
mitments. A more detailed account of our data collection and
analysis strategy is available elsewhere , .
III. RES ULTS
Generating, reﬁning, and sequencing solution concepts, fea-
tures, stories, and eventually the backlog appears to be one
large, tightly-interconnected process that cannot be divided
into neat phases. However, we can divide our observations into
interrelated practices (Section III-A) and obstacles (Section
III-B) that resonate with participants and then abstract them
into a more transferable theory of the backlog (Section III-C).
1https://www.researchgate.net/publication/330823601 The Product
Backlog - Grounded Theory Codes
Fig. 1: Balanced Team
We observed 13 practices related to backlog generation
and reﬁnement. They are summarized in Table I, and their
relationship to the backlog is illustrated in Figure 2. This
section elaborates each practice. While we present the prac-
tices in a neat, idealized sequence for clarity, in reality their
execution order is messy, with parallelism, backtracking, and
1) Balanced Teams and Dual-Track Agile: Pivotal uses two
important organizational systems: “balanced teams” for people
and “dual-track agile” for activities.
Abalanced team, as illustrated in Figure 1, “is an au-
tonomous group of people with a variety of skills and per-
spectives that support each other towards a shared goal” .
Here is how one Pivotal designer explains balanced teams:
“Design is the voice of the users; its main role is to advocate
for the user’s needs. Development is the voice of the system; its
main role is to help the team understand what’s possible and
advocate for good system architecture. Product management
is the voice of the business; its main role is to advocate for
the business’s needs and facilitate prioritization of the team’s
backlog” (Participant 16).
In other words, the teams we observed included members
with diverse backgrounds, who combine business knowledge
(product managers), design skills (product designers), and full-
stack development / testing skills (engineers). All three roles
are needed to keep the team balanced, like the three-legged
stool shown in Figure 1. If a role is under- or over-represented,
the team becomes unbalanced, and the product may suffer.
Meanwhile, dual-track agile is a kind of continuous soft-
ware engineering  in which activities are organized into
two groups. We call these groups “tracks” because they are
continuous and parallel, like train tracks. Track one typi-
cally includes research, negotiating with stakeholders, drawing
user interface mockups, and writing user stories. Track two
typically involves building, testing, architecting, refactoring,
deploying, and maintaining the product. The whole team is
involved in both tracks, but product designers focus on track
one while engineers focus on track two. Ideally, track one
stays ahead of track two so engineers always know what to
do next. These tracks are not phases, and the team does not
iterate between them.
These practices create the need for a backlog. Dual-track
agile creates the boundary that the backlog helps to span.
Balanced teams create the conditions under which a backlog,
rather than a different kind of artifact, is useful for spanning
the boundary between the two tracks.
2) Persona Modeling, Stakeholder Mapping, Interviews,
and Afﬁnity Mapping: We observed product designers leading
the team in deﬁning one or more proto-personas. “A persona
is a description of a ﬁctitious user based on data from user
research” [16, p. 58; emphasis ours]. A proto-persona is a
description of a ﬁctitious user that is based on intuition or
simply made up . Personas typically represent a group of
similar users (or potential users) with a picture, motivational
quote, demographic information, behaviors, needs, and goals.
Participant 56 explains: “When we’re doing exploratory re-
search, we are looking for patterns in people that can form a
persona. We are looking for patterns in what these people do
that are problematic.”
We also observed some product designers leading the team
in drawing a map of a product’s stakeholders—especially in-
dividuals inside the client organization (e.g., product sponsor,
related business units, marketing, executives).
The product designers used their proto-personas and stake-
holder maps to determine who to interview.
We observed product designers interviewing clients, cur-
rent users of an existing system, or people in a particular
demographic (e.g., drivers of Korean cars) who might use a
new system. All observed projects and nearly all surveyed
product designers relied on user interviews. Interviews pri-
marily asked open-ended, non-leading questions; some used
interview scripts or topic maps as guides. In some interviews,
the interviewee would use the product at home, at work, or at
a Pivotal ofﬁce. The entire team often attended.
While the interviewer asked questions, one or more team
members wrote key ideas on sticky notes. Different color
sticky notes were used for each interview to facilitate trace-
Interviewees share their ways of working, beliefs, ideas, val-
ues, attitudes, and preferences; however, interviewees do not
consult an internal directory of enduring preferences. Rather,
the interviewee and interviewer spontaneously co-construct
evanescent preferences . The purpose of these interviews
is to understand the interviewee, not to elicit requirements or
discover solutions. None of the product designers we observed
use the term “requirements.” Participant 42 explained: “My
goal is to listen. Once a client asked me, ‘what do you make
of all the interviews you’ve done so far?’ I reminded the
client, ‘at this stage, my goal is to listen and absorb, my
processing will come soon.”’ Product designers intentionally
resist solution-focused thinking (which they call “solutioniz-
ing”) during interviews.
TABLE I: Practices Related to Product Backlogs
Name Brief Description
Balanced teams Balancing team composition with experts in busi-
ness, product design, and software development.
Dual track agile Organizing the work into two “tracks.” Track 1 typi-
cally includes research, negotiating with stakehold-
ers, drawing user interface mockups, and writing
user stories. Track 2 typically involves building,
testing, architecting, refactoring, deploying, and
maintaining the product.
Drawing a diagram of individuals who are inter-
ested in the success of the product.
Interviewing Semi-structured discussions with stakeholders (e.g.,
users, product sponsor).
Persona modeling Creating ﬁctional users (character sketches) to rea-
son about who will use product features.
Afﬁnity mapping Organizing data from user interviews or ideation
sessions to generate insights.
Design studio Converging on a product concept by iterating be-
tween generating and discussing design ideas.
Drawing informal models of graphical user inter-
Usability testing /
Reviewing mockups with users.
Writing brief, informal descriptions of some aspects
of a software system.
Story showcase Building a shared team understanding of upcoming
Reﬁning and resequencing user stories.
Accepting stories Evaluating delivered work.
Product designers would stop interviewing when they felt
that they understood users’ needs around a particular feature
set and would resume interviewing when additional informa-
tion was needed. “I interview until I do not learn new things
from interviews” (Participant 52). They typically began with
about ﬁve interviews per proto-persona : “I have had
projects where there are multiple personas so I would say
like ﬁve for one persona is good” (Participant 41).
Product designers use interviews to reﬁne their proto-
personas into user personas. They believe that personas help
the team understand and empathise with users, and therefore
create products that better support user needs and goals. “This
builds empathy for your user base. Going through [these
practices] really helps with decision making moving forward”
(Participant 41). Later, they use personas for “persona valida-
tion;” that is, predicting whether the user group represented by
a persona will use a product, feature, or solution concept. Af-
ter interviewing several people, product designers sometimes
realized that the persona would not use the product. The team
then had to choose whether to redesign the product for the
opportunity provided by the target persona (i.e., “invalidate
the product”) or ﬁnd a new demographic that will use the
product concept (i.e., “invalidate the persona”).
We observed teams synthesizing what was learned during
interviews using afﬁnity mapping . Teams clustered sticky
notes from user interviews on a large whiteboard or wall. They
would place and rearrange the notes into cohesive clusters,
leading to “insights” about user needs. Ad hoc, informal
conversations would often trigger rearranging the notes and
clusters. Each new design insight was recorded on a new sticky
note and added to a special “design insights” list. After all the
sticky notes were on the board and all clusters ﬁnalized, the
team would discuss each cluster and its related insights on
user needs. Some teams perform this exercise one interview
at a time to incorporate the data quickly; others do a batch of
interviews to allow the conversations to blend and percolate.
Many participants refer to this process as “synthesis.”
Participants see adding customer requests directly to the
backlog as an anti-pattern: it “creates a Frankenstein amal-
gam of features. Microsoft products exhibit this. Instead, we
want insight-synthesized research. Patterns emerged from the
synthesis of data. Insights earned their way” (Participant 17).
Persona modeling, stakeholder mapping, interviews, and
afﬁnity mapping help the team learn about the domain. This
knowledge informs the user stories or other items that consti-
tute the product backlog.
3) Design Studio, Sketching Mockups, Usability Testing /
Validation Testing, and Writing User Stories: Once product
designers felt that they had a reasonably good understanding
of user needs, they generated solution concepts to satisfy those
needs. For instance, we observed product designers leading
the team in what some participants call “design studio.” Each
member individually sketched mockups of webpages or mobile
screens and illustrated how users would interact with them.
Teams iteratively examined the mockups and drew new ones,
leveraging each others’ ideas. Teams eventually converged
on a promising solution concept. Designers iterated between
reﬁning the mockups and conducting further user interviews
(in which they shared mockups with potential users, provided a
task or goal, watched the users interact with the mockups, and
listened to the user’s feedback). This “usability testing” would
continue until the new feedback seemed inconsequential. It
would either validate or invalidate each feature represented by
As a product and the team’s understanding of users mature,
product designers did less exploratory research and more val-
idation and usability research. They relied more on telemetry
from working software. However, when new assumptions or
questions arose, designers often reverted to interviewing.
In all projects, we observed product managers writing user
stories based on mockups, discussions with product designers,
and their own research. User stories were typically written
only for features with positive usability feedback.
Auser story is a brief, informal description of some aspect
of a software system. Large, vague stories are called epics.
While user stories vary widely between organizations, most
observed stories included a motivation and acceptance criteria.
The brief motivation statement followed the pattern: As a
<user>I want to <action>so that <value>.This is some-
times called the Connextra template . The acceptance crite-
ria followed the pattern: Given <context>, when <condition>
then <action>. This is sometimes called Gherkin syntax .
When working on a new feature, the product manager
would list several story titles (e.g., “Michael logs in,” “Michael
creates an account,” “Michael logs out”) in a sequence and
then elaborate and reﬁne each story, sometimes combining
small stories or splitting up large ones. Product managers
aimed to identify the smallest possible features that still
provide user value. Stories are typically composed directly
in Pivotal’s project management software, Pivotal Tracker.2
Stories are often linked to a relevant mockup: “I try to attach
[mockups] to every story. I try not to write a story before a
[mockup] is done, and that works out really well” (Participant
No simple mapping links user interviews, user needs, design
insights, mockups, epics, and user stories. The team does
not translate interviews into user needs and design insights,
transform insights into mockups, deduce epics from mockups,
or decompose epics into smaller stories. All of these verbs
misrepresent the cognitive process of designing. Such terms
present designing as symbol manipulation—like solving high-
school algebra problems. You cannot simply rearrange inter-
view notes into user needs and design insights. Each insight
entails a creative leap to one small area of an enormous,
ambiguous, multidimensional solution space.
When product designers draw a mockup, they are informed
by their multifaceted understanding of the project context,
including their memories of user interviews, notes, design
insights, education, experience, preconceptions, and biases.
Similarly, when product managers write stories, they are
informed by their multifaceted understanding of the project
context as well as relevant mockups. Each transition is a
predominantly improvised, creative act.
Much has been written about how design thinking
involves—but is not limited to—writing, drawing, problem-
solving, and decision making (cf. , ). Critically, the
evolving solution concept triggers reframing of the project
context. This oscillation between ideas about the project con-
text and ideas about potential solutions is essential to design
thinking (see Section III-C).
Design Studio, sketching mockups, usability testing / val-
idation testing, and writing user stories all contribute to the
generation of backlog items.
4) Story Showcase, Backlog Grooming, and Accepting Sto-
ries: We observed teams meeting to discuss upcoming stories.
The product manager and product designer(s) shared their
understanding of the stories with the engineers. While the
product manager wrote the stories before the meeting, teams
often revised stories, combined smaller stories or broke up
larger ones, and clariﬁed acceptance criteria. The engineers
would examine the acceptance criteria to better understand
what ‘done’ means for each story and discuss the effort
required. During these meetings, team members broadened
2Publicly available at pivotaltracker.com
their shared mental model of the work to be done, and
everyone would leave with a deeper, clearer understanding of
the project context, desired product, and immediate stories.
We call this meeting a story showcase. For historical rea-
sons, some participants call it an “iteration planning meeting,”
even though most projects are iteration-less, and the meeting is
mostly about sharing not planning. Unlike planning meetings
in Extreme Programming and Scrum, the team does not
commit to the work to be done in a speciﬁed period. The
product manager may change or re-prioritize any unstarted
We observed product managers periodically (e.g., right
before a story showcase) examining the backlog to verify that
stories were developer-ready and prioritized to deliver the most
value to the user quickly.
We did not observe product managers prioritizing the en-
tire backlog. Instead, product managers appear to move just
enough high-priority stories to the top of the backlog to
stay ahead of the engineers. While the backlog appears as
a continuous list, there is an imaginary line between the top n
stories, which have been recently prioritized, and stories n+1
onwards, which may have once been prioritized but are now in
no particular order. In some instances, we observed engineers
getting ahead of the product manager and beginning story n+1
without realizing that they had moved into the unprioritized
section of the backlog.
We observed product managers shaping the product di-
rection by resequencing stories based on conversations with
stakeholders about user research and business needs. We also
observed teams asking product managers to resequence some
work to make the story implementation easier (e.g., building
user creation before user login).
Backlog grooming is the process of reviewing and reﬁning
a backlog. It includes both reviewing stories for clarity and
completeness and sequencing (or resequencing) stories.
We use the term accepting stories to denote a process
we observed in which product managers evaluate delivered
work. The product manager would verify that the implemented
story functioned according to the provided acceptance criteria.
If it did not, the product manager would reject the story
Story showcases, backlog grooming, and accepting stories
all involve managing the backlog. The story showcase also
helps the team develop a shared understanding of upcoming
stories in the backlog.
The teams we studied routinely encountered numerous
obstacles revolving around a general theme of stakeholders
misunderstanding the nature of product design. The term
design can refer to many different things even within software
engineering. In the broadest sense, design means specifying
the properties of an object , including what it does (feature
selection), how users interact with it (interaction design), how
it looks (visual design), how it is organized (architectural
design), and exactly how it works (low-level design). Let us
consider some speciﬁc events from our research.
1) Preconceiving Problems: In a government agency
project, Participant 25 felt that the team found the problem
they were told to ﬁnd: “They told us to go and look at grant life
cycle management, so we went and looked at grant life cycle
management, and then, ultimately, we created a prototype for
managing a grant life cycle...” The designer was frustrated
because they saw better ways of framing the problematic
situation, but there was too much momentum around solving
the pre-conceived problem.
This misunderstanding concerns the way design problems
are framed. Software engineering literature often assumes that
the problem to be solved is simply known or given. In reality,
stakeholders often agree that a situation is problematic but
disagree on the exact nature of the problem(s) . Moreover,
framing a problem and generating a solution concept are the
same cognitive process . Overspecifying a problem for the
designer actually impedes effective solution generation .
2) Preconceiving Solutions: A more extreme version of
preconception is a client arriving with a dubious problem and
an ill-considered solution concept. In one project, the client
wanted a healthcare application. They had in mind a very
detailed, extensive feature set and a particular proto-persona
who would use these features. After several interviews with
prospective users, the product designers invalidated the proto-
persona. The client argued that ‘the interviewees who gave us
that feedback were not our real end users’ and identiﬁed a
new proto-persona, which was also invalidated by research.
The client identiﬁed a third proto-persona that was again
invalidated. The client rejected good user research because
it invalidated their business model. The team relented and
built the product the client wanted. A year later, the company
dismissed their engineers because no one used their product.
“The stakeholders can assume that they know what the
problem is and what the solution is before we have really
had time to process what we have learned. We will start
interviewing. People are saying ‘I know it! We do not have
to keep doing this research,’ and I am saying ‘no, no, we have
only talked to two people, we do not know what is there yet,
we do not know if that is the solution” (Participant 41).
At Pivotal speciﬁcally and in the interdisciplinary design
literature more generally, solution concepts emerge from a
complicated process of studying stakeholders, simultaneously
framing a problem and generating ﬂedgling solution concepts,
sharing initial results with stakeholders, and elaborating a
tentative solution. Clients arriving with premature problem
frames or solution concepts, which have not emerged from
careful research and reﬂection, obstruct design.
3) Pressure to Converge: Creative design is closely related
to divergent thinking ; that is, exploring many dissimilar
ideas. Insufﬁciently exploring the design space and converging
prematurely on a poor solution concept is therefore a major
threat to innovation.
We observed teams spending several weeks researching
the problematic context, generating solution concepts, and
validating feature ideas with prospective users. In multiple
projects, we observed teams iterating on many ideas in a short
period, then selecting one with which to move forward. The
period of entertaining divergent ideas was always brief:
“The sketching exercises take us about two to ﬁve hours.
We are divergent with many different ideas, and then we keep
doing it until convergence” (Participant 51).
To the participants, their convergence on a ﬂedgling solution
concept in a few hours does not feel premature. Participants
feel that they resist early “solutionizing,” consider many alter-
natives, and have the ﬂexibility to pivot to another solution
depending on the ﬁndings. Some designers feel that the
diversity of a balanced team leads to more creative solutions:
“Oftentimes, the developers give us the most interesting points
of view; they ﬂip the designer’s model with their sketching”
To an outsider, however, given the complexity of the projects
involved, two to ﬁve hours does not seem like much time
to generate and consider many genuinely innovative solution
concepts. What is clear is the tension between time pressure
and divergence. Some clients push for an aggressive schedule
and dismiss divergent thinking as a waste of time.
Expert designers in other ﬁelds exhibit similar patterns .
Despite the common advice to generate several alternative
solution ideas, expert designers tend to converge rapidly on
a single concept and resist radical reformulations .
4) Ambiguity: We observed participants struggling with
ambiguity on several occasions. Survey respondent 25 said,
“It felt odd to walk in every day not knowing what you would
tackle or try to learn.” Indeed, this happens so often that many
product designers have grown accustomed to stakeholders pan-
icking over ambiguity. Participant 41 explained that “people
can be uncomfortable with how open-ended and exploratory it
is; sometimes the amount of unknown is very uncomfortable.”
A common sequence we observed begins with a client
arriving with some idea about “the problem” and a tentative
solution, and expecting the team to reﬁne and elaborate
them. Instead, user research tends to undermine preconcep-
tions before offering new insights. Structure decreases before
increasing. Participants often ﬁnd this stressful and confusing.
“Most of the time, when clients are upset, it is because
we have not managed the ambiguity well ... [Retaining their]
tangible idea of what the product is going to be is extremely
stressful because their [envisioned] product may not actually
be what is right for them” (Participant 25).
Even when the team does not have a preconceived solution,
embracing ambiguity is challenging. On a disaster assistance
project, an experienced product designer expected the process
to produce more questions and more ambiguity before ﬁnding
a solution. As ambiguity increased from broad user data, the
team became pessimistic while she was optimistic. The team
kept saying “there is nothing to solve,” but she persevered
and discovered “enough nuggets” to design mockups for two
products. The team picked one and released a product that
satisﬁed the users’ needs.
Ambiguity is unavoidable when designing systems for
complex organizations with multiple, quarreling stakeholders.
However, product managers and engineers often lack design
thinking skills and familiarity with this process. Agonizing
over ambiguity appears to affect team morale:
“Product managers struggled with it the most. They are
used to having a goal and being able to work towards that
goal ... Pivotal employees who are very uncomfortable with
the ambiguity make it hard for the team.” —Participant 25
To help the team cope with ambiguity, product designers
may “tell stories and talk about previous experiences; connect
the client to former client PMs who have been there; reassure
that we are on track and the ambiguity is normal.” (Survey
Participant 11). They “help them understand that we are in
this together. We have a process that will help us ﬁnd the best
ideas and solution” (Participant 52).
Participants appear to become more comfortable as they get
more experience with Pivotal’s design process.
5) Time Pressure: Meanwhile, time pressure inhibits the
entire design process. Interviewing users, researching organi-
zations, synthesizing design concepts, and iterating on mock-
ups is time-consuming. Product designers can feel acute pres-
sure to truncate user research and deliver immature mockups
before they have a sophisticated understanding of the project
context. Time pressure is often exacerbated by clients not
understanding the design process and its value or thinking they
have already designed the product. “Sometimes people feel the
anxiety of this taking too long, and push to start coming up
with the solution” (Participant 41).
On the healthcare application mentioned in Section III-B2,
the client arrived with a preconceived solution concept, and the
product designer felt intense pressure to deliver unvalidated
mockups to the engineers. The team coped by having the
engineers focus on conﬁguring the tech stack, adding con-
tinuous integration and easy deployment to the production
environment, and delivering web pages with no styling. In
other words, they delivered something small that was very easy
to change while the designer reﬁned her mockups. However,
this led to substantial stress and rework, which are both
intrinsically wasteful .
6) Blocking Access to Users: In one extreme case, as
Participant 41 explains, “I could not even create a persona
for the customer service agent because we were not allowed
to talk to them.” The project was a call center application.
The client refused to let customer support representatives be
interviewed. The client believed that they were too busy to be
interviewed and that call center metrics and case notes would
be sufﬁcient to inform the new product.
Without access to the users, the product team could not
develop deep insights into how to improve the users’ efﬁ-
ciency. They could not create realistic personas or evaluate
usability. The absent users fundamentally broke the team’s
process. To deliver the product, the team had to speculate
about the personas, needs, and behaviors. The team could
not validate whether the proposed solution would address the
C. Theorizing the Backlog
We observed teams organizing activities into two tracks
with a soft but unmistakable boundary between them. The ﬁrst
track involves researching the project context and generating
solution concepts, mockups, and user stories. The second
track involves delivering a software product based on those
concepts, mockups, and stories.
This observation leads us to adopt Glaser’s cutting point
theoretical coding family, which he describes as indicating
a “boundary, critical juncture, cutting point, turning point,
breaking point, benchmark, division, cleavage... This family
is a variation of the degree family. Degree focuses on the full
range, while here we focus on signiﬁcant breaks or cutting
points on the range. Cutting points are very important in theory
generation since they indicate where the difference occurs
which has differential effects” [11, p. 76].
As illustrated in Figure 2, the product backlog spans the
boundary between the two tracks in dual-track agile. Track
one ﬁlls the backlog (with stories) while track two empties
the backlog (by implementing the stories). Of course, not
every team that has a backlog uses dual-track agile, but a
soft division between generating and implementing solution
concepts is consistent with not only our understanding of
Scrum, Kanban, and Extreme Programming but also our past
observations of both professional and student developers.
The idea of the backlog spanning the boundary between
different kinds of work (and the different kinds of profession-
als who do the work) is crucial to answering the questions
motivating this paper: what is a product backlog, what is its
role, and how does it emerge?
1) What a Product Backlog is: The product backlog is an
informal model of work to be done. The backlogs that we
observed are partially-ordered lists of brief work item de-
scriptions. Some items include effort estimates, dependencies,
or pointers to various artifacts elaborating on them. Because
backlogs are used in agile development, only immediate work
may be sequenced.
As a model, the backlog is useful for externalizing cognition
and memory. A large software system could have thousands
of stories—far too many to consider at once or to remember
for long. The backlog allows, for example, a product manager
to externalize thoughts about a large number of features, see
the big picture, introduce changes, and prioritize effectively.
Similarly, the backlog can help engineers remember what to do
next, drill down into the speciﬁcs of the work to be done (e.g.,
by looking at a user story, mockup, and acceptance criteria),
and recall the details of a discussion several days prior.
A backlog is neither a requirements speciﬁcation nor
a design speciﬁcation. The backlog predominately communi-
cates the desired order of work items. It lists neither design
details nor the needs that features are supposed to meet. The
backlog items (i.e., stories) are reminders of conversations, not
2) The Role of a Backlog: A product backlog is a boundary
object that bridges the gap between generating and implement-
ing user stories.
Fig. 2: Theory of Product Backlogs
A boundary object is an artifact that different people can
use and interpret in different ways . For example, the same
map can be used by hikers to ﬁnd a campsite or biologists to
study animal movements. Boundary objects not only facilitate
knowledge sharing and coordination  but also help groups
cooperate without consensus .
We observed teams using backlog grooming and story
showcase meetings to build a shared understanding of the
product vision and work to be done. User stories both facilitate
this conversation and later provide a reminder of it. Seeing
the backlog as a boundary object helps us understand the
difference between a backlog and a speciﬁcation: a good
speciﬁcation replaces face-to-face communication while a
good backlog facilitates face-to-face communication.
Conceptualizing the product backlog as a boundary object
assumes that it mediates communication between different
groups or roles . In our observations, the backlog fa-
cilitated communication between professionals with different
backgrounds: product designers, product managers, and engi-
3) Where Backlogs Come From: Backlogs emerge from
three interconnected processes:
1) Sensemaking. Pivotal product designers typically de-
ploy several practices—including stakeholder mapping,
interviewing, persona modeling, and afﬁnity mapping—
to learn about the domain. Regardless of whether a
project is outsourced, offshored, or in-house, “To convert
a problematic situation to a problem, a practitioner
must... make sense of an uncertain situation that initially
makes no sense” [25, p. 40]. In sociology and manage-
ment as well as software engineering, this activity is
called sensemaking , .
2) Context-solution Coevolution. A designer’s thoughts
oscillate between a tentative problem frame and
ﬂedgling solutions, such that understanding of the prob-
lematic context and solution concepts coevolve ,
, . We observed coevolution in both afﬁnity
mapping (where the teams oscillated between writing
design insights and re-organizing needs categories) and
design studios (where the sketching of mockups trig-
gered reconceptualizations of the project context and
vice versa). We observed teams continuing to reﬁne
mockups and their understanding of the context after
usability testing and while writing stories.
3) Boundary Spanning. While the product designers lead
track one and engineers lead track two, we observed
product managers leading the team in deploying several
practices—including backlog grooming, story showcas-
ing and accepting stories—to bridge the gap between
the two tracks. Participant 1 explained: “You meet every
week, and you feel like you have this check in... I feel
like this helps bridge the gap between what’s going on
in the design world and the dev world. Having those
check-ins really help.” Following previous research, we
call this category boundary spanning , .
To be clear, the two tracks of dual-track agile and the bound-
ary spanning between them are ongoing, parallel processes, not
This section evaluates our study using appropriate quality
criteria, including those proposed by Charmaz .
1) Credibility: “Is there sufﬁcient data to merit claims?”
This study relies on two years and ﬁve months of participant-
observation, 56 intensive open-ended interviews, an informal
questionnaire, and archival records.
2) Originality: “Do the categories offer new insights?” This
article provides the ﬁrst empirically grounded explanation of
how product backlogs are generated. The deﬁnition of the
backlog and the misunderstanding design obstacle are also
3) Resonance: “Does the theory make sense to partic-
ipants?” We presented the results to the organization. Six
participants reviewed the results and this paper for member
checking . They indicated that the results and theory
resonate with their experience; for example:
•“This paper combines all the things that were in my head
and helped me to connect dots” (Participant 52).
•“Context-solution coevolution speaks to the back and
forth nature of learning about this speciﬁc problem
(context) and testing a solution’s impact (if any) in a
continuous way” (Participant 56).
•“It gave me a different relationship to the backlog by
putting it between the 2 tracks” (Participant 57).
This process produced no signiﬁcant changes, which is not
surprising because participant observation mitigates resonance
4) Usefulness: “Does the theory offer useful interpreta-
tions?” Researchers can better understand agile development
by viewing the backlog as a boundary object and model of
work to be done. (See also Section VI.)
5) Researcher Bias and Prior Knowledge Bias: Partici-
pant observers develop an insider’s perspective; they both
notice and miss things an outsider would not. Similarly, prior
knowledge can not only help the researcher interpret events
and select lines of inquiry but also blind the researcher to
alternative explanations . We mitigated these risks by
recording interviews and having the second and third authors
review the coding process.
6) Transferability: Grounded Theory is non-statistical, non-
sampling research; therefore, results do not statistically gen-
eralize to a population. However, as we organized our ob-
servations into higher-level categories, much of what seems
speciﬁc to Pivotal and dual-track agile receded, leaving pro-
cesses that appear highly transferable to other teams that use
backlogs. Sensemaking and context-solution coevolution have
been established by previous research as practically univer-
sal phenomena in design generally and software engineering
speciﬁcally , , . The research on boundary objects
and spanners suggests that boundary spanning is fundamental
to contemporary software engineering—or at least projects
where professionals with different roles and backgrounds
collaborate . It should not matter:
•whether the backlog is held in project management soft-
ware or made of physical cards;
•whether the team uses dual-track agile, Scrum or another
•whether most backlog items are user stories, use cases,
•whether the team adopt the practices (e.g., user inter-
views, afﬁnity maps) described above;
•whether the team is distributed or co-located;
•whether the code is closed-source or open-source.
Moreover, it does not matter whether or not the system has
a graphical user interface and human users. API or command
line systems simply need a different kind of product designer.
The proposed concepts should transfer well to any team that:
(1) adopts an agile approach; (2) has a backlog of work items;
and (3) has at least one team member who brings business
knowledge or design skills.
However, our depiction of dual-track agile assumes an ill-
structured problem domain. In structured problem-solving,
there is one problem, everyone agrees what the one prob-
lem is, and the meaning of the problem is clear (e.g., the
travelling salesman problem). In structured problem-solving,
sensemaking is trivial, context-solution coevolution does not
occur, and boundary spanning is unnecessary. The danger
here is mistaking ill-structured problems for well-structured
ones. As we have described, just because a client arrives with
what appears to be a clear problem description or even a
comprehensive design speciﬁcation, that does not mean the
problem is well-structured.
V. RE LATE D WOR K
It appears that our current understanding of a product
backlog as a list of work items for software developers was
invented or at least popularized by proponents of Scrum.
Although this is difﬁcult to determine with certainty, the
earliest reference to a modern product backlog we can ﬁnd
is Schwaber’s presentation at OOPSLA 1995 .
The Scrum Guide deﬁnes “the Product Backlog [as] an
ordered list of everything that is known to be needed in
the product” . Ambler extends this deﬁnition to include
different kinds of work items such as training or reviewing
other teams’ products  and therefore calls it a “work item
list.” Scrum’s and Ambler’s view of the backlog broadly align
with our observations in Section III-C1.
The presentation of the product backlog in this article is
consistent with the Scrum literature in several ways:
•The backlog is a collection of work items.
•The backlog is evolving throughout a project.
•The backlog is continually reﬁned or groomed by the
Product Owner (in Scrum) / Product Manager (at Pivotal).
However, the Scrum literature presents the backlog in
several ways that are inconsistent with the observations and
conceptualization presented here:
•Scrum deﬁnes the backlog as an ordered list; all of the
backlogs we observed were only partially ordered.
•The Scrum Guide states: “At any point in time, the total
work remaining to reach a goal can be summed” .
The backlogs we observed all contained items that were
neither estimated nor fully ﬂeshed out.
•Some presentations of Scrum, including the Scrum Guide,
state that the backlog contains requirements. Require-
ments formally describe needs. User stories informally
describe features. None of the backlog items we ob-
served were requirements. Conceptualizing a backlog
as a requirements speciﬁcation is inconsistent with our
observations (see Section III-C).
Beyond the prescriptive agile literature, some studies have
investigated how backlog items are prioritized. A systematic
review of 13 such studies found no consensus about how items
should be prioritized  and concluded that “the average
quality of studies is low” (p. 5).
Racheva et al.  synthesized a lifecycle theory of backlog
prioritization from existing literature. However, lifecycle the-
ories are typically inappropriate for modeling human behav-
ior . Indeed, weighed against the observations reported
here, this model appears to overrationalize the prioritization
process in several ways:
1) It assumes backlog items are requirements. Again, this
is not what we observed.
2) It presents the steps of prioritization as a sequential
process with no backtracking. In contrast, we observed
product managers periodically grooming the backlog.
3) It does not explain where the backlog items come from.
We found only one backlog-focused experiment, which
showed that students using the work system snapshot concep-
tual modeling technique wrote better backlog items .
Of course, many agile development case studies mention a
backlog, but we are not aware of any cases that focus on the
backlog. Furthermore, we are not aware of any research that
speciﬁcally investigates how backlog items are generated.
The Scrum literature, for example, shies away from how
exactly to generate user stories or other backlog items. Scrum
simply assigns the job of writing user stories to the “Product
Owner” but says little about user research, the creative process
of designing the system, or writing and reﬁning these stories
, , .
Extreme Programming  similarly offers little guidance for
determining what to build. In the Real Customer Involvement
practice , an onsite customer provides features through
story cards. This does not provide design guidance; it simply
transfers design responsibility to customers.
The Human Computer Interaction (HCI) community, in
contrast, is chieﬂy concerned with the design of user expe-
riences for interactive products. HCI incorporated the concept
of sensemaking as early as 1993 , and many of the
practices described above are well-known in user-centered
design . These processes and practices remain decoupled
from mainstream SE methods . Some in the SE community
are consequently under “the impression that the user-centered
design approach and usability engineering methods are only
for decorating a thin component sitting on top of the software
or the ‘real’ system” .
Several systematic literature reviews , ,  have
recently attempted to correct this misunderstanding and bridge
the gap between the SE community’s focus on how to build
and the HCI community’s focus on what to build. For example,
Brhel et al.  identify ﬁve principles underlying user-
centered agile development: (1) separate product discovery
and product creation, (2) iterative and incremental design and
development, (3) parallel interwoven tracks of design and
development, (4) continuous stakeholder involvement, and (5)
artifact-mediated communication. Garcia et al.  further
investigate principle (5) and concluded that user stories are
one of the main artifacts mediating communication.
A signiﬁcant ﬁnding that has emerged from these literature
reviews, as well as this study, is the idea of interwoven
tracks of determining what to build and building it—that is,
dual-track agile. The term dual-track agile originates from
articles describing software development at Alias (now part of
Autodesk)  . Both papers depict two tracks (called
the Developer Track and the Interaction Designer Track),
with designers working at least one or two cycles ahead of
developers. (We did not adopt these labels because, in our
observations, track one was not limited to interaction design,
and all team members participated in both tracks.)
Recently, several professional articles present various ac-
counts of dual-track agile , . These articles suggest
that, similar to Pivotal, many companies perceive dual-track
agile as a promising approach for bridging the gap between
generating solution concepts and building software.
This article reports the ﬁrst in-depth ﬁeld study focused on
the product backlog. It makes the following contributions:
1) It clariﬁes that a product backlog is an informal model of
work to be done (and not a requirements speciﬁcation).
2) It explains how a backlog functions as a boundary
object: that is, bridging the gap between generating
solution concepts and building software.
3) It explains how backlogs emerge from sensemaking,
context-solution coevolution, and boundary spanning.
4) It describes 13 speciﬁc practices that give rise to product
backlogs at Pivotal.
5) It describes design obstacles faced by product designers
These contributions are important because, despite the ubiq-
uity of product backlogs, no scholarly research has comprehen-
sively explained how they emerge or function. Software teams
can use the practice descriptions to improve how they create
their backlogs. They can also use the obstacle descriptions to
explain to upper management and clients why user research
is needed and why structure and clarity often decrease before
increasing. Educators can use the theory of the backlog to
help students understand what a backlog is and how it works.
Researchers can use this work to understand the backlog’s
relationship to requirements and design decisions. More re-
search is needed to determine how to assess backlog quality
and establish its effects on software engineering success.
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