Optimal Release Time: Numbers or Intuition?

Despite the exponential increase in the demand for software and the increase in our dependence on software, many software manufacturers behave in an unpredictable manner. In such an unpredictable software manufacturer organization, it is difficult to determine the optimal release time. An economic model is presented supporting the evaluation and comparison of different release or market entry alternatives. This model requires information with respect to achieved reliability and maintainability. Existing literature reveals many models to estimate reliability and limited models to estimate maintainability. The practicality of most available models is however criticized. A series of case studies confirmed that software manufacturers struggle with determining the reliability and maintainability of their products prior to releasing them. This leads to a combination of non-analytical methods to decide when a software product is good enough for release: intuition prevails where sharing convincing information is required. Next research steps are put forward to investigate ways increasing the economic reasoning about the optimal release time

Taxonomy Based Testing and Validation of a new Defect Classification for Health Software

Defect-based testing is a powerful tool for finding errors in software. Many software manufacturers avoid this method because it requires a detailed defect taxonomy that is expensive to construct and difficult to validate. The Association for the Advancement of Medical Instrumentation (AAMI) is developing SW911, a defect taxonomy to be published as a standard for health software. This paper details three methods to validate SW91 for its comprehensiveness. The initial validations of SW91 were conducted via mapping vulnerabilities from the Common Weakness Enumeration and a dataset from a medical device software development company in Ireland. Taxonomy based testing is another validation method proposed in this research and its applicability was investigated using empirical data from a medical device software development company in Ireland. Finally, the paper details future plans to implement taxonomy based testing to improve software quality in medical device software and to validate SW91. This validation will focus on the efficiency, reliability, ability to perform useful analyses and defect coverage of SW91

Підхід до моделювання якості сімейств програмних систем

Аналізуються проблеми моделювання якості сімейств програмних систем (СПС), визначаються вимоги до моделі якості СПС та пропонується підхід до моделювання, який полягає у комбінуванні різнотипних моделей відповідно до рівнів архітектури СПС (ієрархічних, аналітичних та байєсівських моделей), а також методу аналізу ієрархій для встановлення пріоритетів вимог до характеристик якості і наступного визначення інтегрального показника якості СПС.Проанализированы проблемы моделирования качества семейств программных систем (СПС). Определены требования к модели качества СПС. Предложен подход к моделированию, который заключается в комбинировании разнотипных моделей в соответствии с уровнем архитектуры СПС (иерархических, аналитических и байесовских моделей), а также метода анализа иерархий для установления приоритетов требований к характеристикам качества и последующего определения интегрального показателя качества СПС.Problems of software family quality modeling are analysed. Requirements for the software family quality model are determined and an approach for the modelling consisting in combination of various models according to the architecture levels (hieratic, analytical and Bayesian models) of software family is offered, as well as analytic hierarchy method for the prioritization of quality requirements and next determination of integral quality characteristic of software family

System Qualities Ontology, Tradespace and Affordability (SQOTA) Project Phase 5

Motivation and Context: One of the key elements of the SERC's research strategy is transforming the practice of systems engineering and associated management practices- "SE and Management Transformation (SEMT)." The Grand Challenge goal for SEMT is to transform the DoD community 's current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first ,document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise-oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08-D-0171 and HQ0034-13-D-0004 (TO 0060).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08-D-0171 and HQ0034-13-D-0004 (TO 0060)

Software Product Quality Models, Developments, Trends and Evaluation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Software product quality models have evolved in their abilities to capture and describe the abstract notion of software quality since the 1970’s. Many models constructed deal with a specific part of software quality only which makes them ineligible to assess the quality of software products as a whole. Former publications failed to thoroughly examine and list all the available models which attempt to describe each known property of software product quality. This paper discovers such complete software product quality models published since 2000; moreover, it endeavours to measure the relevance of each model quantitatively by introducing indicators with regard to the scientific and industrial community. The identified 23 software product quality model classes differ significantly in terms of publication intensity, publication range, quality score average, relevance score and the 12-month average of the Google Relative Search Index. The results offer a foundation for selecting the appropriate software product quality model for use or for extension if newly identified quality properties need to be connected to a general context. Furthermore, the experiences accumulated on the field of software product quality modelling motivated researchers to successfully transfer the concepts to other areas where abstract entities need to be compared or assessed including the quality of higher educational teaching and business processes, which is also briefly highlighted in the paper

Practical Consequences of Quality Views in Assessing Software Quality

open access articleThe authors’ previously published research delved into the theory of software product quality modelling, model views, concepts, and terminologies. They also analysed this specific field from the point of view of uncertainty, and possible descriptions based on fuzzy set theory and fuzzy logic. Laying a theoretical foundation was necessary; however, software professionals need a more tangible and practical approach for their everyday work. Consequently, the authors devote this paper to filling in this gap; it aims to illustrate how to interpret and utilise the previous findings, including the established taxonomy of the software product quality models. The developed fuzzy model’s simplification is also presented with a Generalized Additive Model approximation. The paper does not require any formal knowledge of uncertainty computations and reasoning under uncertainty, nor does it need a deep understanding of quality modelling in terms of terminology, concepts, and meta-models, which were necessary to prepare the taxonomy and relevance ranking. The paper investigates how to determine the validity of statements based on a given software product quality model; moreover, it considers how to tailor and adjust quality models to the particular project’s needs. The paper also describes how to apply different software product quality models for different quality views to take advantage of the automation potential offered for the measurement and assessment of source code quality. Furthermore, frequent pitfalls are illustrated with their corresponding resolutions, including an unmeasured quality property that is found to be important and needs to be included in the measurement and assessment process

Quality Properties of Execution Tracing, an Empirical Study

The authors are grateful to all the professionals who participated in the focus groups; moreover, they also express special thanks to the management of the companies involved for making the organisation of the focus groups possible.Data are made available in the appendix including the results of the data coding process.The quality of execution tracing impacts the time to a great extent to locate errors in software components; moreover, execution tracing is the most suitable tool, in the majority of the cases, for doing postmortem analysis of failures in the field. Nevertheless, software product quality models do not adequately consider execution tracing quality at present neither do they define the quality properties of this important entity in an acceptable manner. Defining these quality properties would be the first step towards creating a quality model for execution tracing. The current research fills this gap by identifying and defining the variables, i.e., the quality properties, on the basis of which the quality of execution tracing can be judged. The present study analyses the experiences of software professionals in focus groups at multinational companies, and also scrutinises the literature to elicit the mentioned quality properties. Moreover, the present study also contributes to knowledge with the combination of methods while computing the saturation point for determining the number of the necessary focus groups. Furthermore, to pay special attention to validity, in addition to the the indicators of qualitative research: credibility, transferability, dependability, and confirmability, the authors also considered content, construct, internal and external validity

Determining Organization-specific Process Suitability

Abstract. Having software processes that fit technological, project, and business demands is one important prerequisite for software-developing organizations to operate successfully in a sustainable way. However, many such organizations suffer from processes that do not fit their demands, either because they do not provide the necessary support, or because they provide features that are no longer necessary. This leads to unnecessary costs during the development cycle, a phenomenon that worsens over time. This paper presents the SCOPE approach for systematically determining the process demands of current and future products and projects, for analyzing existing processes aimed at satisfying these demands, and for subsequently selecting those processes that provide the most benefit for the organization. The validation showed that SCOPE is capable of adjusting an organization's process scope in such a way that the most suitable processes are kept and the least suitable ones can be discarded

Maintainability of classes in terms of bug prediction

Measuring software product maintainability is a central issue in software engineering which led to a number of different practical quality models. Besides system level assessments it is also desirable that these models provide technical quality information at source code element level (e.g. classes, methods) to aid the improvement of the software. Although many existing models give an ordered list of source code elements that should be improved, it is unclear how these elements are affected by other important quality indicators of the system, e.g. bug density. In this paper we empirically investigate the bug prediction capabilities of the class level maintainability measures of our ColumbusQM probabilistic quality model using open-access PROMSIE bug dataset. We show that in terms of correctness and completeness, ColumbusQM competes with statistical and machine learning prediction models especially trained on the bug data using product metrics as predictors. This is a great achievement in the light of that our model needs no training and its purpose is different (e.g. to estimate testability, or development costs) than those of the bug prediction models