Journal of Software: Evolution and Process 

About: Journal of Software: Evolution and Process is an academic journal published by Wiley. The journal publishes majorly in the area(s): Software development & Software. It has an ISSN identifier of 1532-060X. Over the lifetime, 764 publications have been published receiving 12653 citations. The journal is also known as: Journal of Software Evolution and Process & Journal of Software: Evolution and Process.

Feature location in source code: a taxonomy and survey

Abstract: SUMMARY Feature location is the activity of identifying an initial location in the source code that implements functionality in a software system. Many feature location techniques have been introduced that automate some or all of this process, and a comprehensive overview of this large body of work would be beneficial to researchers and practitioners. This paper presents a systematic literature survey of feature location techniques. Eighty-nine articles from 25 venues have been reviewed and classified within the taxonomy in order to organize and structure existing work in the field of feature location. The paper also discusses open issues and defines future directions in the field of feature location. Copyright © 2011 John Wiley & Sons, Ltd.

Software reconnaissance: mapping program features to code

Abstract: Maintainers of old code often need to discover where particular program features are implemented. This paper presents a method, called ‘software reconnaissance’ for answering this question through an analysis of the execution of different test cases. The method is quite easy to implement, requiring only a test coverage monitor, some simple tools, and a surprisingly small number of test cases. A statistical case study is presented that shows the kind of results that can be obtained on a typical medium-sized program. The method seems to be effective in identifying a small number of code components that are unique to a particular program feature, though it may not find all components that make up the feature's delocalized plan. A small protocol case study shows that professional programmers can learn to use the method quickly and can use the information that it produces. Software reconnaissance may be a simple but useful addition to the maintainer's tool kit in that it provides starting points for understanding a large program and a way of recovering some requirements traceability information from old code. For the researcher, it also provides a novel functionality ‘view’ of software that maps features to program components at different levels of precision.

Software maintenance management: changes in the last decade

Abstract: Compared to a decade ago, when the first comprehensive study was done in software maintenance, many changes have occurred in the practice of system development. Longitudinal data were obtained by using the same survey instrument, updated to reflect current practices, and sampling the same population. Comparing the current with 1977 results has helped to identify the persistent problems and issues as well as the emerging problems and issues. One of the important, but somewhat disturbing, conclusions is that maintenance problems are pretty much the same as during the 1970s (except for minor changes), despite advances made in structured methodologies and techniques. In terms of specific problems, personnel problems of maintenance programmers, i.e. turnover and availability, and programmer effectiveness problems, i.e. skills, motivation and productivity, have shown a rise, while problems associated with users' knowledge of computer systems have declined.

Global software engineering and agile practices: a systematic review

Abstract: Agile practices have received attention from industry as an alternative to plan-driven software development approaches. Agile encourages, for example, small self-organized collocated teams, whereas global software engineering (GSE) implies distribution across cultural, temporal, and geographical boundaries. Hence, combining them is a challenge. A systematic review was conducted to capture the status of combining agility with GSE. The results were limited to peer-reviewed conference papers or journal articles, published between 1999 and 2009. The synthesis was made through classifying the papers into different categories (e.g. publication year, contribution type, research method). At the end, 81 papers were judged as primary for further analysis. The distribution of papers over the years indicated that GSE and Agile in combination has received more attention in the last 5 years. However, the majority of the existing research is industrial experience reports in which Agile practices were modified with respect to the context and situational requirements. The emergent need in this research area is suggested to be developing a framework that considers various factors from different perspectives when incorporating Agile in GSE. Practitioners may use it as a decision-making basis in early phases of software development.

Combining text mining and data mining for bug report classification

Abstract: Bug reports represent an important information source for software construction. Misclassification of these reports inevitably introduces bias. Manual examinations can help reduce the noise, but bring a heavy burden for developers instead. In this paper, we propose a multi-stage approach by combining both text mining and data mining techniques to automate the prediction process. The first stage leverages text mining techniques to analyze the summary parts of bug reports and classifies them into three levels of probability. The extracted features and some other structured features of bug reports are then fed into the machine learner in the second stage. Data grafting techniques are employed to bridge the two stages. Comparative experiments with previous studies on the same data-three large-scale open-source projects-consistently achieve a reasonable enhancement from 77.4% to 81.7%, 76.1% to 81.6%, and 87.4% to 93.7%, respectively over their best results in terms of overall performance. Additional comparative empirical experiments on other seven popular open-source systems confirm the findings. Moreover, based on the data obtained, we also empirically studied the impact relation between the underlying classifiers and various other properties of the combined model. A prototypical recommender system has been developed to demonstrate the applicability of our approach. Copyright © 2016 John Wiley & Sons, Ltd.