Code Quality Evaluation Methodology Using The ISO/IEC 9126 Standard

This work proposes a methodology for source code quality and static behaviour evaluation of a software system, based on the standard ISO/IEC-9126. It uses elements automatically derived from source code enhanced with expert knowledge in the form of quality characteristic rankings, allowing software engineers to assign weights to source code attributes. It is flexible in terms of the set of metrics and source code attributes employed, even in terms of the ISO/IEC-9126 characteristics to be assessed. We applied the methodology to two case studies, involving five open source and one proprietary system. Results demonstrated that the methodology can capture software quality trends and express expert perceptions concerning system quality in a quantitative and systematic manner.Comment: 20 pages, 14 figure

Using the ISO/IEC 9126 product quality model to classify defects : a Controlled Experiment

Background: Existing software defect classification schemes support multiple tasks, such as root cause analysis and process improvement guidance. However, existing schemes do not assist in assigning defects to a broad range of high level software goals, such as software quality characteristics like functionality, maintainability, and usability. Aim: We investigate whether a classification based on the ISO/IEC 9126 software product quality model is reliable and useful to link defects to quality aspects impacted. Method: Six different subjects, divided in two groups with respect to their expertise, classified 78 defects from an industrial web application using the ISO/IEC 9126 quality main characteristics and sub-characteristics, and a set of proposed extended guidelines. Results: The ISO/IEC 9126 model is reasonably reliable when used to classify defects, even using incomplete defect reports. Reliability and variability is better for the six high level main characteristics of the model than for the 22 sub- characteristics. Conclusions: The ISO/IEC 9126 software quality model provides a solid foundation for defect classification. We also recommend, based on the follow up qualitative analysis performed, to use more complete defect reports and tailor the quality model to the context of us

Using quality models in software package selection

The growing importance of commercial off-the-shelf software packages requires adapting some software engineering practices, such as requirements elicitation and testing, to this emergent framework. Also, some specific new activities arise, among which selection of software packages plays a prominent role. All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages' capabilities. There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback.Peer ReviewedPostprint (published version

Using Automatic Static Analysis to Identify Technical Debt

The technical debt (TD) metaphor describes a tradeoff between short-term and long-term goals in software development. Developers, in such situations, accept compromises in one dimension (e.g. maintainability) to meet an urgent demand in another dimension (e.g. delivering a release on time). Since TD produces interests in terms of time spent to correct the code and accomplish quality goals, accumulation of TD in software systems is dangerous because it could lead to more difficult and expensive maintenance. The research presented in this paper is focused on the usage of automatic static analysis to identify Technical Debt at code level with respect to different quality dimensions. The methodological approach is that of Empirical Software Engineering and both past and current achieved results are presented, focusing on functionality, efficiency and maintainabilit

Investigating Automatic Static Analysis Results to Identify Quality Problems: an Inductive Study

Background: Automatic static analysis (ASA) tools examine source code to discover "issues", i.e. code patterns that are symptoms of bad programming practices and that can lead to defective behavior. Studies in the literature have shown that these tools find defects earlier than other verification activities, but they produce a substantial number of false positive warnings. For this reason, an alternative approach is to use the set of ASA issues to identify defect prone files and components rather than focusing on the individual issues. Aim: We conducted an exploratory study to investigate whether ASA issues can be used as early indicators of faulty files and components and, for the first time, whether they point to a decay of specific software quality attributes, such as maintainability or functionality. Our aim is to understand the critical parameters and feasibility of such an approach to feed into future research on more specific quality and defect prediction models. Method: We analyzed an industrial C# web application using the Resharper ASA tool and explored if significant correlations exist in such a data set. Results: We found promising results when predicting defect-prone files. A set of specific Resharper categories are better indicators of faulty files than common software metrics or the collection of issues of all issue categories, and these categories correlate to different software quality attributes. Conclusions: Our advice for future research is to perform analysis on file rather component level and to evaluate the generalizability of categories. We also recommend using larger datasets as we learned that data sparseness can lead to challenges in the proposed analysis proces

A DISCUSSION ON ASSURING SOFTWARE QUALITY IN SMALL AND MEDIUM SOFTWARE ENTERPRISES: AN EMPIRICAL INVESTIGATION

Under the studies of general core activities including software inspection, review and testing to achieve quality objectives in small-medium size enterprises (SMEs), the paper presents a contemporary view of such companies against quality measures. The results from a local empirical investigation of quality standards in the Turkish software industry are reported.Around 150 software companies have been approached from which 17 detailed feedback inform that in order to ensure software quality, standards including internationally recognized International Standards Organization (ISO) and Capability Maturity Model Integration (CMMI) are given credit. However the substantial workload and resources required to obtain them are also reported as serious; downscaled frameworks of such large models proposed in the literature are not well known by the SMEs either. The paper also discusses "work around" that bypasses such standards to ease delivery of products while keeping certificates as labels just to acquire new jobs for the business

A quality management based on the Quality Model life cycle

Managing quality is a hard and expensive task that involves the execution and control of processes and techniques. For a good quality management, it is important to know the current state and the objective to be achieved. It is essential to take into account with a Quality Model that specifies the purposes of managing quality. QuEF (Quality Evaluation Framework) is a framework to manage quality in MDWE (Model-driven Web Engineering). This paper suggests managing quality but pointing out the Quality Model life cycle. The purpose is to converge toward a quality continuous improvement by means of reducing effort and time.Ministerio de Ciencia e Innovación TIN2010-20057-C03-02Ministerio de Ciencia e Innovación TIN 2010-12312-EJunta de Andalucía TIC-578