Objectives and Key Results in Software Teams: Challenges, Opportunities
and Impact on Development
- URL: http://arxiv.org/abs/2311.00236v1
- Date: Wed, 1 Nov 2023 02:39:01 GMT
- Title: Objectives and Key Results in Software Teams: Challenges, Opportunities
and Impact on Development
- Authors: Jenna Butler, Thomas Zimmermann, Christian Bird
- Abstract summary: Middle management seems to be a critical component of the translation of lofty goals to actionable work items.
In addition, attitudes and beliefs of engineers are critical to the success of any goal setting framework.
- Score: 10.103741812151592
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Building software, like building almost anything, requires people to
understand a common goal and work together towards it. In large software
companies, a VP or Director will have an idea or goal and it is often the job
of middle management to distill that lofty, general idea into manageable,
finite units of work. How do organizations do this hard work of setting and
measuring progress towards goals? To understand this question, we undertook a
mixed methods approach to studying goal setting, management dissemination of
goals, goal tracking and ultimately software delivery at a large multi-national
software company.
Semi-structured interviews with 47 participants were analyzed and used to
develop a survey which was deployed to a multi-national team of over 4,000
engineers. The 512 responses were analyzed using thematic analysis, linear
regressions and hypothesis testing, and found that tracking, measuring and
setting goals is hard work, regardless of tools used. Middle management seems
to be a critical component of the translation of lofty goals to actionable work
items. In addition, attitudes and beliefs of engineers are critical to the
success of any goal setting framework. Based on this research, we make
recommendations on how to improve the goal setting and OKR process in software
organizations: invest in the data pipeline, increase transparency, improve
communication, promote learning communities, and a structured roll out of OKRs.
Related papers
- Sentiment Analysis Tools in Software Engineering: A Systematic Mapping Study [43.44042227196935]
We aim to help developers or stakeholders in their choice of sentiment analysis tools for their specific purpose.
Our results summarize insights from 106 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools, (5) the usage of already existing tools, and (6) the difficulties researchers face.
arXiv Detail & Related papers (2025-02-11T19:02:25Z) - LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS.
It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset.
GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
arXiv Detail & Related papers (2025-01-27T03:29:44Z) - Towards Measuring Goal-Directedness in AI Systems [0.0]
A key prerequisite for AI systems pursuing unintended goals is whether they will behave in a coherent and goal-directed manner.
We propose a new family of definitions of the goal-directedness of a policy that analyze whether it is well-modeled as near-optimal for many reward functions.
Our contribution is a definition of goal-directedness that is simpler and more easily computable in order to approach the question of whether AI systems could pursue dangerous goals.
arXiv Detail & Related papers (2024-10-07T01:34:42Z) - Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in Instructional Videos [48.15438373870542]
VidAssist is an integrated framework designed for zero/few-shot goal-oriented planning in instructional videos.
It employs a breadth-first search algorithm for optimal plan generation.
Experiments demonstrate that VidAssist offers a unified framework for different goal-oriented planning setups.
arXiv Detail & Related papers (2024-09-30T17:57:28Z) - Using Agile Story Points and Game Theory Together: Better Software Planning and Development in Agile Software Development [0.0]
This study integrates key concepts from Agile software development, Story Point estimation, and Game Theory.
We propose the application of game theoretic strategies, notably the Vickrey Auction and Stag Hunt Game.
arXiv Detail & Related papers (2024-08-30T18:32:14Z) - Discrete Factorial Representations as an Abstraction for Goal
Conditioned Reinforcement Learning [99.38163119531745]
We show that applying a discretizing bottleneck can improve performance in goal-conditioned RL setups.
We experimentally prove the expected return on out-of-distribution goals, while still allowing for specifying goals with expressive structure.
arXiv Detail & Related papers (2022-11-01T03:31:43Z) - Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning [15.33496710690063]
We propose goal-aware cross-entropy (GACE) loss, that can be utilized in a self-supervised way.
We then devise goal-discriminative attention networks (GDAN) which utilize the goal-relevant information to focus on the given instruction.
arXiv Detail & Related papers (2021-10-25T14:24:39Z) - C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks [133.40619754674066]
Goal-conditioned reinforcement learning can solve tasks in a wide range of domains, including navigation and manipulation.
We propose the distant goal-reaching task by using search at training time to automatically generate intermediate states.
E-step corresponds to planning an optimal sequence of waypoints using graph search, while the M-step aims to learn a goal-conditioned policy to reach those waypoints.
arXiv Detail & Related papers (2021-10-22T22:05:31Z) - Automatic Curriculum Learning through Value Disagreement [95.19299356298876]
Continually solving new, unsolved tasks is the key to learning diverse behaviors.
In the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency.
We propose setting up an automatic curriculum for goals that the agent needs to solve.
We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
arXiv Detail & Related papers (2020-06-17T03:58:25Z) - Mutual Information-based State-Control for Intrinsically Motivated
Reinforcement Learning [102.05692309417047]
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal.
In the natural world, intelligent organisms learn from internal drives, bypassing the need for external signals.
We propose to formulate an intrinsic objective as the mutual information between the goal states and the controllable states.
arXiv Detail & Related papers (2020-02-05T19:21:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.