SYSTEMATIC APPROACHES FOR ORGANISATIONAL LEARNING - A LITERATURE REVIEW

The activity of developing high-quality information systems (IS) is a highly volatile and knowledge- intensive process. Nonetheless, only few IS developing companies seem to be advanced in evaluating and processing their knowledge. Despite a variety of existing approaches, there is no systematic overview of these and how far they support organisational learning. We conduct a systematic literature review of highly ranked journals and relevant textbooks to provide such an overview. We provide a list of eight systematic learning approaches and analyse how they contribute to the activities of knowledge creation, retention and transfer. Thereby, we aim to improve the current situation of organisational learning in IS developing companies. Whereas organisations need to become more open to systematic organisational learning approaches, research is in need to evaluate existing approaches and develop holistic strategies for building learning organisations

Utilizing End-user Requirements to Inform the Knowledge Supply Strategies of IT Project Teams

This research investigates the knowledge sourcing requirements of teams that implement novel IT projects. It then compares those requirements to the mainstream strategy proffered in the literature for knowledge reuse within project environments. Using a grounded theory approach, this research found that the knowledge sourcing requirements do not align with the mainstream strategy, which is based on a codification approach. Rather, the findings indicate the teams that implement novel IT projects rely primarily on a personalization strategy for sourcing complex, incipient, and sensitive knowledge and the Internet for sourcing simple knowledge. These teams generally did not use internal knowledge repositories to fulfill their knowledge sourcing needs

Proactive Empirical Assessment of New Language Feature Adoption via Automated Refactoring: The Case of Java 8 Default Methods

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations in the area. Here, we explore Java 8 default methods, which allow interfaces to contain (instance) method implementations. Default methods can ease interface evolution, make certain ubiquitous design patterns redundant, and improve both modularity and maintainability. A focus of this work is to discover, through a scientific approach and a novel technique, situations where developers found these constructs useful and where they did not, and the reasons for each. Although several studies center around assessing new language features, to the best of our knowledge, this kind of construct has not been previously considered. Despite their benefits, we found that developers did not adopt default methods in all situations. Our study consisted of submitting pull requests introducing the language feature to 19 real-world, open source Java projects without altering original program semantics. This novel assessment technique is proactive in that the adoption was driven by an automatic refactoring approach rather than waiting for developers to discover and integrate the feature themselves. In this way, we set forth best practices and patterns of using the language feature effectively earlier rather than later and are able to possibly guide (near) future language evolution. We foresee this technique to be useful in assessing other new language features, design patterns, and other programming idioms

A Taxonomy of Information System Projects’ Knowledge-sharing Mechanisms

Despite its criticality to the success of information system (IS) projects, knowledge sharing among IS projects is generally ineffective compared to knowledge sharing in IS projects. Although several mechanisms for knowledge sharing exist in the literature, it is difficult to determine which mechanism one should use in a specific context. We lack work that concisely and comprehensively classifies these mechanisms. Based on a literature review, we extracted information from 33 studies and identified twelve mechanisms for sharing knowledge among IS projects. Then, we derived a taxonomy for these mechanisms, which extends previous research by both adapting existing mechanisms and complementing the set of dimensions used for their classification. The results help to systematically structure the fields of knowledge management and IS projects. Both research and practice can use this taxonomy to better understand knowledge in this domain and effectively adopt mechanisms for a particular application

A golden age for working with public proteomics data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature 'omics' disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets

Proactive Empirical Assessment of New Language Feature Adoption via Automated Refactoring: The Case of Java 8 Default Methods

Programming languages and platforms improve over time, sometimes resulting in new language features that offer many benefits. However, despite these benefits, developers may not always be willing to adopt them in their projects for various reasons. In this paper, we describe an empirical study where we assess the adoption of a particular new language feature. Studying how developers use (or do not use) new language features is important in programming language research and engineering because it gives designers insight into the usability of the language to create meaning programs in that language. This knowledge, in turn, can drive future innovations in the area. Here, we explore Java 8 default methods, which allow interfaces to contain (instance) method implementations. Default methods can ease interface evolution, make certain ubiquitous design patterns redundant, and improve both modularity and maintainability. A focus of this work is to discover, through a scientific approach and a novel technique, situations where developers found these constructs useful and where they did not, and the reasons for each. Although several studies center around assessing new language features, to the best of our knowledge, this kind of construct has not been previously considered. Despite their benefits, we found that developers did not adopt default methods in all situations. Our study consisted of submitting pull requests introducing the language feature to 19 real-world, open source Java projects without altering original program semantics. This novel assessment technique is proactive in that the adoption was driven by an automatic refactoring approach rather than waiting for developers to discover and integrate the feature themselves. In this way, we set forth best practices and patterns of using the language feature effectively earlier rather than later and are able to possibly guide (near) future language evolution. We foresee this technique to be useful in assessing other new language features, design patterns, and other programming idioms

A Model for Capturing and Managing Software Engineering Knowledge and Experience

During software development projects there is always a particular working "product" that is generated but rarely managed: the knowledge and experience that team members acquire. This knowledge and experience, if conveniently managed, can be reused in future software projects and be the basis for process improvement initiatives. In this paper we present a model for managing the knowledge and experience team members acquire during software development projects in a non-disruptive way, by integrating its management into daily project activities. The purpose of the model is to identify and capture this knowledge and experience in order to derive lessons learned and proposals for best practices that enable an organization to preserve them for future use, and support software process improvement activities. The main contribution of the model is that it enables an organization to consider knowledge and experience management activities as an integral part of its software projects, instead of being considered, as it was until now, as a follow-up activity that is (infrequently) carried out after the end of the projects