scholarly journals Synthesis of Code Anomalies: Revealing Design Problems in the Source Code

2020 ◽  
Author(s):  
Willian N. Oizumi ◽  
Alessandro F. Garcia
Keyword(s):  
Software Engineering ◽  
Source Code ◽  
New Technique ◽  
Software Systems ◽  
Identification Task ◽  
Software Projects ◽  
Design Problems ◽  
Engineering Community ◽  
Synthesis Technique ◽  
A New Technique

Design problems affect most software projects and make their maintenance expensive and impeditive. Thus, the identification of potential design problems in the source code – which is very often the only available and upto-date artifact in a project – becomes essential in long-living software systems. This identification task is challenging as the reification of design problems in the source code tend to be scattered through several code elements. However, stateof-the-art techniques do not provide enough information to effectively help developers in this task. In this work, we address this challenge by proposing a new technique to support developers in revealing design problems. This technique synthesizes information about potential design problems, which are materialized in the implementation under the form of syntactic and semantic anomaly agglomerations. Our evaluation shows that the proposed synthesis technique helps to reveal more than 1200 design problems across 7 industry-strength systems, with a median precision of 71% and a median recall of 78%. The relevance of our work has been widely recognized by the software engineering community through 2 awards and 7 publications in international and national venues.

scholarly journals COMPARISON OF SOFTWARE COMPLEXITY OF SEARCH ALGORITHM USING CODE BASED COMPLEXITY METRICS

2021 ◽  
Vol 6 (5) ◽  
Author(s):  
Bello Muriana ◽  
Ogba Paul Onuh
Keyword(s):  
Software Engineering ◽  
Programming Languages ◽  
Search Algorithm ◽  
Source Code ◽  
Search Algorithms ◽  
Binary Search ◽  
Software Systems ◽  
Software Complexity ◽  
Complexity Metrics ◽  
Binary Search Algorithm

Measures of software complexity are essential part of software engineering. Complexity metrics can be used to forecast key information regarding the testability, reliability, and manageability of software systems from study of the source code. This paper presents the results of three distinct software complexity metrics that were applied to two searching algorithms (Linear and Binary search algorithm). The goal is to compare the complexity of linear and binary search algorithms implemented in (Python, Java, and C++ languages) and measure the sample algorithms using line of code, McCabe and Halstead metrics. The findings indicate that the program difficulty of Halstead metrics has minimal value for both linear and binary search when implemented in python. Analysis of Variance (ANOVA) was adopted to determine whether there is any statistically significant differences between the search algorithms when implemented in the three programming languages and it was revealed that the three (3) programming languages do not vary considerably for both linear and binary search techniques which implies that any of the (3) programming languages is suitable for coding linear and binary search algorithms.

A Method for Detecting Bad Smells and its Application to Software Engineering Education

2015 ◽  
Vol 3 (2) ◽  
pp. 13-23
Author(s):  
Yuki Ito ◽  
Atsuo Hazeyama ◽  
Yasuhiko Morimoto ◽  
Hiroaki Kaminaga ◽  
Shoichi Nakamura ◽  
...  
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Engineering Education ◽  
Source Code ◽  
Software Systems ◽  
Large Source ◽  
Software Engineering Education ◽  
Meta Programming

In order to extend and maintenance software systems, it is necessary to remove factors behind bad smells from source code through refactoring. However, it is time-consuming process to detect and remove factors behind bad smells manually from large source code. And, learning how to refactor bad smells can be difficult for students because they are not yet software development experts. Therefore, the authors propose a method for detecting bad smells using declarative meta programming that can be applied to software development training. In this manner, software development training is facilitated.

scholarly journals NODE MIXING AND GROUP STRUCTURE OF COMPLEX SOFTWARE NETWORKS

2014 ◽  
Vol 17 (07n08) ◽  
pp. 1450022 ◽  
Author(s):  
LOVRO ŠUBELJ ◽  
SLAVKO ŽITNIK ◽  
NELI BLAGUS ◽  
MARKO BAJEC
Keyword(s):  
Social Networks ◽  
Software Engineering ◽  
Biological Networks ◽  
Group Structure ◽  
Software Systems ◽  
Node Degree ◽  
The Internet ◽  
Intrinsic Properties ◽  
Software Projects ◽  
Practical Applications

Large software projects are among most sophisticated human-made systems consisting of a network of interdependent parts. Past studies of software systems from the perspective of complex networks have already led to notable discoveries with different applications. Nevertheless, our comprehension of the structure of software networks remains to be only partial. Here we investigate correlations or mixing between linked nodes and show that software networks reveal dichotomous node degree mixing similar to that recently observed in biological networks. We further show that software networks also reveal characteristic clustering profiles and mixing. Hence, node mixing in software networks significantly differs from that in, e.g., the Internet or social networks. We explain the observed mixing through the presence of groups of nodes with common linking pattern. More precisely, besides densely linked groups known as communities, software networks also consist of disconnected groups denoted modules, core/periphery structures and other. Moreover, groups coincide with the intrinsic properties of the underlying software projects, which promotes practical applications in software engineering.

scholarly journals Making the Evolution of Graphical Models Visible

10.29007/fqc6 ◽  
2018 ◽  
Author(s):  
Thomas Baar ◽  
Issam Bendaas
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Graphical Models ◽  
Software Industry ◽  
Source Code ◽  
Version Control ◽  
Software Projects ◽  
History Of ◽  
Development Practice ◽  
Uml Models

With adoption of the UML and other graphical languages by software industry,graphical models became a cornerstone in today's software development practice.As other artefacts such as program source code, graphical models evolve overtime and are, thus, put regularily under version control.In order to deeply understand the role an artefact plays within a project, it issometimes helpful to review the history of this artefact. While there arenumerous tools available that make it easy for a user to grasp the evolution oftextual files (or even portions of it), an adequate support for graphical fileshas remained to be an area of niche products.In this paper, we argue that a better support for reviewing the version historyof graphical files can facilitate the work with graphical models. In order tosupport this claim, we implemented a prototypcical tool that can extract anddisplay the version history of any graphical file stored in a GitHub-repository.In addition, users can annotate each version of a file with comments, whatturns our tool into a review tool for software projects. Recently, we started touse the tool is a software engineering course to give students better feedbackon complex UML models they have to develop iteratively.

scholarly journals An Analysis of the PROMISE and ISBSG Software Engineering Data Repositories

2014 ◽  
Vol 13 (5) ◽  
pp. 4456-4474 ◽  
Author(s):  
Laila Cheikhi ◽  
Alain Abran
Keyword(s):  
Software Engineering ◽  
Empirical Studies ◽  
Software Projects ◽  
Data Repositories ◽  
Engineering Community ◽  
Additional Information ◽  
Engineering Data ◽  
Predictor Models ◽  
Industrial Needs

There exist two ongoing large repositories of software projects in the software engineering community: the repository of the International Software Benchmarking Standards Group (ISBSG) and the Repository referred to as PROMISE (PRedictOr Models In Software Engineering). Researchers interested in using the datasets have to conduct their own analysis of the datasets within these repositories that figure out what contents are suitable for their purposes. Repositories designed without users (researchers and Industrial) needs in mind greatly are more challenging to use. This paper present an analysis of both repositories and provide users with additional information on these datasets by identifying the topics addressed, highlighting their descriptiveness, and their availability, and by indicating whether or not further details are available in order to enhance their reusability in further empirical studies. Recommendations to both PROMISE managers and datasets owners are also suggested to improve the usefulness of the data provided.

scholarly journals SWEBOS – The Software Engineering Body of Skills

2015 ◽  
Vol 5 (1) ◽  
pp. 20 ◽  
Author(s):  
Yvonne Sedelmaier ◽  
Dieter Landes
Keyword(s):  
Software Engineering ◽  
Research Design ◽  
Software Engineer ◽  
Soft Skills ◽  
Level Structure ◽  
Data Driven ◽  
Software Systems ◽  
Technical Knowledge ◽  
Software Projects ◽  
Technical Competencies

The development of complex software systems requires a mixture of various technical and non-technical competencies. While some guidelines exist which technical knowledge is required to make a good software engineer, there is a lack of insight as to which non-technical or soft skills are necessary to master complex software projects. This paper proposes a body of skills (SWEBOS) for soft-ware engineering. The collection of necessary skills is developed on the basis of a clear, data-driven research design. The resulting required soft skills for software engineering are described precisely and semantically rich in a three-level structure. This approach guarantees that skills are not just characterized in a broad and general manner, but rather they are specifically adapted to the domain of software engineering.

scholarly journals Differential Testing of Static Analyzers

2020 ◽  
Author(s):  
Gabor Horvath ◽  
Reka Nikolett Kovacs ◽  
Peter Szecsi
Keyword(s):  
Open Source Software ◽  
Ad Hoc ◽  
Source Code ◽  
Software Systems ◽  
False Alarms ◽  
Software Projects ◽  
Industrial Project ◽  
Related Language ◽  
Language Constructs ◽  
Bug Finding

Program faults, best known as bugs, are practically unavoidable in today's ever growing software systems. One increasingly popular way of eliminating them, besides tests, dynamic analysis, and fuzzing, is using static analysis based bug-finding tools. Such tools are capable of finding surprisingly sophisticated bugs automatically by inspecting the source code. Their analysis is usually both unsound and incomplete, but still very useful in practice, as they can find non-trivial problems in a reasonable time (e.g. within hours, for an industrial project) without human intervention Because the problems that static analyzers try to solve are hard, usually intractable, they use various approximations that need to be fine-tuned in order to grant a good user experience (i.e. as many interesting bugs with as few distracting false alarms as possible). For each newly introduced heuristic, this normally happens by performing differential testing of the analyzer on a lot of widely used open source software projects that are known to use related language constructs extensively. In practice, this process is ad hoc, error-prone, poorly reproducible and its results are hard to share. We present a set of tools that aim to support the work of static analyzer developers by making differential testing easier. Our framework includes tools for automatic test suite selection, automated differential experiments, coverage information of increased granularity, statistics collection, metric calculations, and visualizations, all resulting in a convenient, shareable HTML report.

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