Product data management practices in a Bangladeshi agrochemical company

Abstract. As businesses are relying more on information systems to carry out their processes, data is becoming an increasingly important factor for success. In particular, product data is necessary for tasks such as producing, selling, delivering, and invoicing a product within these systems. In the past, studies on product data and product data management management have primarily focused on product development and related activities, with little emphasis on PDM in other stages of a product’s lifecycle. The aim of this Master’s thesis is to explain the contribution of PDM in enhancing a company’s performance by improving its operational and business processes as well as the difficulties and requirements involved in implementing Product Data Management (PDM) practices in a Bangladeshi agrochemical company. The research encompasses overall comprehension of PDM as a company-wide initiative and suggests possible strategies for establishing company-wide PDM practices. To improve their data management practices for handling a broad range of varying products, the case company was surveyed and analyzed in this study. The author utilized a case study approach and conducted interviews to gather data from practitioners with firsthand experience and perspectives. This empirical data has contributed to a better understanding of company-wide PDM. The findings of this research suggest that standardized understanding of products throughout a company is necessary to facilitate effective management of product data. To establish effective PDM practices throughout a company, it is crucial to have a comprehensive understanding of the nature of product data, which encompasses both product master data and general product data from different stakeholder viewpoints. When dealing with a wide range of products that need to be effectively managed, higher level product decisions have a considerable influence on product data management, and general guidelines may be vital for ease of management. The study emphasizes the significance of adopting a top-down approach for creating effective PDM practices, and the need for a generic product structure to facilitate consistent product management. The main contribution of this research is its guidance for managers in establishing true company-wide practices for managing product data

Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic Data

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed in-formation for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://bit.ly/3jN1pJ

A conceptual model for unifying variability in space and time: Rationale, validation, and illustrative applications

With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments

Predicting Software Revision Outcomes on Github Using Structural Holes Theory

Many software repositories are hosted publicly online via social platforms. Online users contribute to the software projects not only by providing feedback and suggestions, but also by submitting revisions to improve the software quality. This study takes a close look at revisions and examines the impact of social media networks on the revision outcome. A novel approach with a mix of different research methods (e.g., ego-centric social network analysis, structural holes theory and survival analysis) is used to build a comprehensible model to predict the revision outcome. The predictive performance is validated using real life datasets obtained from GitHub, the social coding website, which contains 32,962 pull requests to submit revisions, 20,399 distinctive software project repositories, and a social network of 234,322 users. Good predictive performance has been achieved with an average AUC of 0.84. The results suggest that a repository host's position in the ego network plays an important role in determining the duration before a revision is accepted. Specifically, hosts that are positioned in between densely connected social groups are likely to respond more quickly to accept the revisions. The study demonstrates that online social networks are vital to software development and advances the understanding of collaboration in software development research. The proposed method can be applied to support decision making in software development to forecast revision duration. The result also has several implications for managing project collaboration using social media

A Layered Architecture for Uniform Version Management

Version management is a key function of software configuration management (SCM). A big variety of version models has been realized in both commercial systems and research prototypes. These version models differ with respect to the objects put under version control (files, directories, entitles, objects), the organization of versions (version graphs rs. multi-dimensional version spaces), the granularity of versioning (whole software products rs. individual components), emphasis on states rs. emphasis on changes (staters. change-based versioning), rules for version selection, etc. We present UVM, a Uniform Version Model - and its support architecture - for SCM. Unlike other unification approaches such as e.g. UML for object-oriented modeling, we do not assemble all the concepts having been introduced in previous systems. Instead, we define a base model that is built on a small number of concepts. Specific version models may be expressed in terms of this base model. Our approac