Abstract Bad requirements quality can cause expensive consequences during the software development lifecycle, especially if iterations are long and feedback comes late. We aim at a light-weight static requirements analysis approach that allows for rapid checks immediately when requirements are written down. We transfer the concept of code smells to requirements engineering as Requirements Smells . To evaluate the benefits and limitations, we define Requirements Smells, realize our concepts for a smell detection in a prototype called Smella and apply Smella in a series of cases provided by three industrial and a university context. The automatic detection yields an average precision of 59 at an average recall of 82 with high variation. The evaluation in practical environments indicates benefits such as an increase of the awareness of quality defects. Yet, some smells were not clearly distinguishable. Lightweight smell detection can uncover many practically relevant requirements defects in a reasonably precise way. Although some smells need to be defined more clearly, smell detection provides a helpful means to support quality assurance in requirements engineering, for instance, as a supplement to reviews.
The concept of code smells was, to the best of our knowledge, first proposed by Fowler and Beck @cite_31 to answer the question at which point the quality of code is so low that it must be refactored. According to Fowler and Beck, the answer cannot be objectively measured, but we can look for certain concrete, visible symptoms, such as duplicated code @cite_31 as an indicator for bad maintainability @cite_6 . This concept of smells, as well as the list that Fowler and Beck proposed, led to a large field of research. @cite_5 provide an in-depth analysis of the state of the art in code smells. The metaphor of smells as concrete symptoms has since then been transferred to quality of other artifacts including (unit) test smells @cite_54 and smells for system tests in natural language @cite_64 . @cite_35 , further characterize different defects of use cases through the term use case smell. In our work, we extend the notion of smells to the broader context of requirements engineering and introduce a concrete definition for the term .
@cite_35 @cite_64 @cite_54 @cite_6 @cite_5 @cite_31
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Refactoring can have a direct influence on reducing the cost of software maintenance through changing the internal structure of the source-code to improve the overall design that helps the present and future programmers evolve and understand a system. Bad smells are a set of design problems with refactoring identified as a solution. Locating these bad smells has been described as more a human intuition than an exact science. This paper addresses the issue of identifying the characteristics of a bad smell through the use of a set of software metrics. Then by using a pre-defined set of interpretation rules to interpret the software metric results applied to Java source-code, the software engineer can be provided with significant guidance as to the location of bad smells. These issues are addressed in a number of ways. Firstly, a precise definition of bad smells is given from the informal descriptions given by the originators Fowler and Beck. The characteristics of the bad smells have been used to define a set of measurements and interpretation rules for a subset of the bad smells. A prototype tool has been implemented to enable the evaluation of the interpretation rules in two case studies. In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smell-introducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smell-introducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities. identified 22 Code Bad Smells to direct the effective refactoring of code. These are increasingly being taken up by software engineers. However, the empirical basis of using Code Bad Smells to direct refactoring and to address ‘trouble’ in code is not clear, i.e., we do not know whether using Code Bad Smells to target code improvement is effective. This paper aims to identify what is currently known about Code Bad Smells. We have performed a systematic literature review of 319 papers published since identified Code Bad Smells (2000 to June 2009). We analysed in detail 39 of the most relevant papers. Our findings indicate that Duplicated Code receives most research attention, whereas some Code Bad Smells, e.g., Message Chains, receive little. This suggests that our knowledge of some Code Bad Smells remains insufficient. Our findings also show that very few studies report on the impact of using Code Bad Smells, with most studies instead focused on developing tools and methods to automatically detect Code Bad Smells. This indicates an important gap in the current knowledge of Code Bad Smells. Overall this review suggests that there is little evidence currently available to justify using Code Bad Smells. Copyright © 2010 John Wiley & Sons, Ltd. Tests are central artifacts of software systems and play a crucial role for software quality. In system testing, a lot of test execution is performed manually using tests in natural language. However, those test cases are often poorly written without best practices in mind. This leads to tests which are not maintainable, hard to understand and inefficient to execute. For source code and unit tests, so called code smells and test smells have been established as indicators to identify poorly written code. We apply the idea of smells to natural language tests by defining a set of common Natural Language Test Smells (NLTS). Furthermore, we report on an empirical study analyzing the extent in more than 2800 tests of seven industrial test suites.