Context: In a previous study, we reported on a systematic literature review (SLR), based on a manual search of 13 journals and conferences undertaken in the period 1st January 2004 to 30th June 2007. Objective: The aim of this on-going research is to provide an annotated catalogue of SLRs available to software engineering researchers and practitioners. This study updates our previous study using a broad automated search. Method: We performed a broad automated search to find SLRs published in the time period 1st January 2004 to 30th June 2008. We contrast the number, quality and source of these SLRs with SLRs found in the original study. Results: Our broad search found an additional 35 SLRs corresponding to 33 unique studies. Of these papers, 17 appeared relevant to the undergraduate educational curriculum and 12 appeared of possible interest to practitioners. The number of SLRs being published is increasing. The quality of papers in conferences and workshops has improved as more researchers use SLR guidelines. Conclusion: SLRs appear to have gone past the stage of being used solely by innovators but cannot yet be considered a main stream software engineering research methodology. They are addressing a wide range of topics but still have limitations, such as often failing to assess primary study quality.

https://romisatriawahono.net/wp-content/uploads/2013/01/Kitchenham-Systematic-literature-reviews-in-software-engineering-%E2%80%93-A-tertiary-study-2012.pdf

Systematic literature reviews in software engineering - A tertiary study

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

/pdf/software-engineering-for-self-adaptive-systems-a-second-vyp99zhddq.pdf

Software Engineering for Self-Adaptive Systems : A Second Research Roadmap

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

/pdf/comprehensive-review-of-artificial-neural-network-4sb2zr5avn.pdf

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Every programmer has a characteristic style, ranging from preferences about identifier naming to preferences about object relationships and design patterns. Coding conventions define a consistent syntactic style, fostering readability and hence maintainability. When collaborating, programmers strive to obey a project’s coding conventions. However, one third of reviews of changes contain feedback about coding conventions, indicating that programmers do not always follow them and that project members care deeply about adherence. Unfortunately, programmers are often unaware of coding conventions because inferring them requires a global view, one that aggregates the many local decisions programmers make and identifies emergent consensus on style. We present NATURALIZE, a framework that learns the style of a codebase, and suggests revisions to improve stylistic consistency. NATURALIZE builds on recent work in applying statistical natural language processing to source code. We apply NATURALIZE to suggest natural identifier names and formatting conventions. We present four tools focused on ensuring natural code during development and release management, including code review. NATURALIZE achieves 94 % accuracy in its top suggestions for identifier names. We used NATURALIZE to generate 18 patches for 5 open source projects: 14 were accepted.

/pdf/learning-natural-coding-conventions-czipeffx5p.pdf

Learning natural coding conventions

This paper explores the issue of whether the field of IS is in crisis. To do so, the paper first starts by looking back on where the field has come from. Next, it assesses the status of the IS field by exploring where the field is now. That our current status remains a ‘fragmented
adhocracy’ suggests the field may indeed be in crisis or headed for a crisis. This is compounded by the fact that there are two different views on the state of the IS field, each posing its own set of threats. One is the external view of the community (the view of IS from outside the academic field); the other is the internal view (the view from inside the IS community). By analyzing these two views, a better understanding of the problems the field faces emerges. In the next part of the paper, some thoughts are presented on where might the field go from here for overcoming its internal communication deficit. The paper proposes four different types of knowledge for structuring an IS Body of Knowledge (BoK) and
following on from that, the value of creating a common BoK for the field. Lastly, the implications arising from the paper’s analysis are explored. More specifically, the paper
considers various options that are available for overcoming the internal communications deficit the IS field faces. These include changing the way the field thinks about generalizations, changing the institutional publication practices, focusing more on understanding the field’s organizational stakeholders, and developing new knowledge creation and transformation networks. If IS as a field can overcome its internal communications deficits, it might ultimately contribute to the societal challenge of developing a deliberative cyber democracy and thereby help to address the social communication deficit which is a feature of modern mass societies.

Crisis in the IS field? A critical reflection on the state of the discipline

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Guide to the software engineering body of knowledge