Software product quality requirements engineering method: soquarem
Bibliographic record
Abstract
The IT industry needs reliable data about Requirements (QRs) to adequately evaluate systems and their architecture. management of the software product is an emerging discipline aiming to enhance the software product quality by addressing its quality requirements. Dealing with this kind of is not easy and implies much effort from practitioners, better involvement of interested stakeholders and a solid knowledge in quality management techniques. In fact they are vague, difficult to define and often conflict with other requirements. New approaches toward QRs management are developed to resolve problems of traditional software engineering views as: a) lack of systematic guidelines on how to elicit QRs; b) difficulty to identify QRs and to represent them in models and processes. In the context of a proposal for a SOftware product QUAlity Requirements Engineering Method (SOQUAREM), this thesis provides a structured QRs engineering process with its supporting ISO/IEC SQuaRE 25030 standard, management techniques and concepts. SOQUAREM process spans 2 high levels of abstraction (business and system) and six conceptual phases such as: identification and refinement of business goals, derivation and consolidation of the quality attributes and their integration into the functional process. The proposed SOQUAREM illustrates in a structured and easy to use way how several concepts can be combined at different organizational levels to identify, represent, document and retrace quality attributes. This document is divided into six chapters: the first chapter presents a background and related work on Quality requirements in general and on various quality management methods such as MOQARE (Misuse-Oriented QuAlity Requirements Engineering)), IESE NFR (Institute for Experimental Software Engineering Non Functional Requirements), Soft Goal Notation (Chung Framework), FDAF (Formal Design and Analysis Framework) and ATAM (Architecture Tradeoff Analysis Method). The second chapter introduces the research topic with its objectives, its limits, the research methodology and research steps. The third chapter describes the research execution by analyzing the current situation of quality with the resulted indicators from academic and industrial environments and formulating the future of the proposed research solution. An overview of the innovative aspects of proposed method like its specific features, metamodel, building process, and process structure are pinpointed. The fourth chapter describes primarily the most important parts of the research which are the development of a new quality engineering method called SOftware product QUAlity Requirements Engineering Method including fundamentals, key concepts and a process model. The fifth chapter presents an illustrative example applied to a building automation system called MSLite. Applicability of SOQUAREM process in this example is developed and analyzed. The last chapter presents a conclusion on this research work and its expected evolution in the future.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".