Software, Volume 2, Issue 1 (March 2023) – 6 articles

GitHub and GitLab contain many project repositories. Each repository contains many design artifacts and specific project management features. Developers can automate the processes of design and project management with the approach proposed in this paper. We described the knowledge base model and diagnostic analytics method for the solving of design automation and project management tasks. This paper also presents examples of use cases for applying the proposed approach. Full article

Scientific data acquisition is a problem domain that has been underserved by its computational tools despite the need to efficiently use hardware, to guarantee validity of the recorded data, and to rapidly test ideas by configuring experiments quickly and inexpensively. High-dimensional physical spectroscopies, such as angle-resolved photoemission spectroscopy, make these issues especially apparent because, while they use expensive instruments to record large data volumes, they require very little acquisition planning. The burden of writing data acquisition software falls to scientists, who are not typically trained to write maintainable software. In this paper, we introduce AutodiDAQt to address these shortfalls in the scientific ecosystem. To ground the discussion, we demonstrate its merits for angle-resolved photoemission spectroscopy and high bandwidth spectroscopies. AutodiDAQt addresses the essential needs for scientific data acquisition by providing simple concurrency, reproducibility, retrospection of the acquisition sequence, and automated user interface generation. Finally, we discuss how AutodiDAQt enables a future of highly efficient machine-learning-in-the-loop experiments and analysis-driven experiments without requiring data acquisition domain expertise by using analysis code for external data acquisition planning. Full article

Compliance in business processes has become a fundamental requirement given the constant rise in regulatory requirements and competitive pressures that have emerged in recent decades. While in other areas of business process modelling and execution, considerable progress towards automation has been made (e.g., process discovery, executable process models), the interpretation and implementation of compliance requirements is still a highly complex task requiring human effort and time. To increase the level of “mechanization” when implementing regulations in business processes, compliance research seeks to formalize compliance requirements. Formal representations of compliance requirements should, then, be leveraged to design correct process models and, ideally, would also serve for the automated detection of violations. To formally specify compliance requirements, however, multiple process perspectives, such as control flow, data, time and resources, have to be considered. This leads to the challenge of representing such complex constraints which affect different process perspectives. To this end, current approaches in business process compliance make use of a varied set of languages. However, every approach has been devised based on different assumptions and motivating scenarios. In addition, these languages and their presentation usually abstract from real-world requirements which often would imply introducing a substantial amount of domain knowledge and interpretation, thus hampering the evaluation of their expressiveness. This is a serious problem, since comparisons of different formal languages based on real-world compliance requirements are lacking, meaning that users of such languages are not able to make informed decisions about which language to choose. To close this gap and to establish a uniform evaluation basis, we introduce a running example for evaluating the expressiveness and complexity of compliance rule languages. For language selection, we conducted a literature review. Next, we briefly introduce and demonstrate the languages’ grammars and vocabularies based on the representation of a number of legal requirements. In doing so, we pay attention to semantic subtleties which we evaluate by adopting a normative classification framework which differentiates between different deontic assignments. Finally, on top of that, we apply Halstead’s well-known metrics for calculating the relevant characteristics of the different languages in our comparison, such as the volume, difficulty and effort for each language. With this, we are finally able to better understand the lexical complexity of the languages in relation to their expressiveness. In sum, we provide a systematic comparison of different compliance rule languages based on real-world compliance requirements which may inform future users and developers of these languages. Finally, we advocate for a more user-aware development of compliance languages which should consider a trade off between expressiveness, complexity and usability. Full article

It has become increasingly preferable to construct bespoke software development processes according to the specifications of the project at hand; however, defining a separate process for each project is time consuming and costly. One solution is to use a Software Process Line (SPrL), a specialized Software Product Line (SPL) in the context of process definition. However, instantiating an SPrL is a slow and error-prone task if performed manually; an adequate degree of automation is therefore essential, which can be achieved by using a Model-Driven Development (MDD) approach. Furthermore, we have identified specific shortcomings in existing approaches for SPrL Engineering (SPrLE). To address the identified shortcomings, we propose a novel MDD approach specifically intended for SPrLE; this approach can be used by method engineers and project managers to first define an SPrL, and then construct custom processes by instantiating it. The proposed approach uses a modeling framework for modeling an SPrL, and applies transformations to provide a high degree of automation when instantiating the SPrL. The proposed approach addresses the shortcomings by providing an adequate coverage of four activities, including Feasibility analysis, Enhancing the core process, Managing configuration complexity, and Post-derivation enhancement. The proposed approach has been validated through an industrial case study and an experiment; the results have shown that the proposed approach can improve the processes being used in organizations, and is rated highly as to usefulness and ease of use. Full article

In the literature, infinite-failure software reliability models (SRMs), such as Musa-Okumoto SRM (1984), have been demonstrated to be effective in quantitatively characterizing software testing processes and assessing software reliability. This paper primarily focuses on the infinite-failure (type-II) non-homogeneous Poisson process (NHPP)-based SRMs and evaluates the performances of these SRMs comprehensively by comparing with the existing finite-failure (type-I) NHPP-based SRMs. In more specific terms, to describe the software fault-detection time distribution, we postulate 11 representative probability distribution functions that can be categorized into the generalized exponential distribution family and the extreme-value distribution family. Then, we compare the goodness-of-fit and predictive performances with the associated 11 type-I and type-II NHPP-based SRMs. In numerical experiments, we analyze software fault-count data, collected from 16 actual development projects, which are commonly known in the software industry as fault-count time-domain data and fault-count time-interval data (group data). The maximum likelihood method is utilized to estimate the model parameters in both NHPP-based SRMs. In a comparison of the type-I with the type-II, it is shown that the type-II NHPP-based SRMs could exhibit better predictive performance than the existing type-I NHPP-based SRMs, especially in the early stage of software testing. Full article