Binary Linear Programming-based Release Planning for Multi-tenant Business SaaS
Bibliographic record
Abstract
In multi-tenant Software as a Service (SaaS) business software, the degree of tenants' satisfaction is a significant indicator of the success of the SaaS system. Tenants' satisfaction can be achieved by continuously fulfilling their evolving needs. Usually, SaaS providers frequently deliver new releases of the application. Each release contains new or enhanced features. However, SaaS providers have limited resources, which makes it difficult to them to incorporate all of the tenants' requests in the next release. Therefore, some requirements shall be postponed to later releases. In order to achieve the highest possible level of tenets' satisfaction, SaaS providers shall include the most common requirements in the next release, which guarantee the satisfaction of highest possible number of tenants with less effort. Additionally, tenants' priorities and preferences about the requirements must be considered. Besides maximizing tenants' satisfaction, it is crucial to meet different types of constraints such as resource, technical, and contractual constraints. This paper identifies the factors that govern the release planning process for multi-tenant business software, which are contractual constraints, commonality of requirements, tenants' preferences and decision weights, risk, technical constraints. The first two factors are suggested by this paper, while the remaining factors are inherited from the traditional release planning process. Moreover, this paper proposes a framework that deals with the uniqueness of the release planning process in multi-tenant SaaS system. In this framework, Binary Linear Programming (BLP) is employed to optimize the selection process of the requirements that will be implemented in the next release. An experiments section is provided to illustrate the degree of satisfaction that can be achieved using the proposed framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".