Marcos Kalinowski

Evidence-Based Guidelines to Defect Causal Analysis

Default causal analysis (DCA) or defect prevention is required by higher-maturity-level software development processes such as the Brazilian Software Process Improvement Reference Model and Capability Maturity Model Integration. The authors ask and answer questions about implementing it in lower-maturity organizations. In the related web extra entitled “Evidence-Based Guidelines on Defect Causal Analysis,” authors Marcos Kalinowski, David N. Card, and Guilherme H. Travassos discuss the basics of research protocol.

Towards a Defect Prevention Based Process Improvement Approach

Defect causal analysis (DCA) is a means of product focused software process improvement. A systematic literature review to identify the DCA state of the art has been undertaken. The systematic review gathered unbiased knowledge and evidence and identified opportunities for further investigation. Moreover, some guidance on how to efficiently implement DCA in software organizations could be elaborated. This paper describes the initial concept of the DBPI (Defect Based Process Improvement) approach. It represents a DCA based approach for process improvement, designed considering the results of the systematic review and the obtained guidance. Its main contributions are tailoring support for DCA based process improvement and addressing an identified opportunity for further investigation by integrating organizational learning mechanisms regarding cause-effect relations into the conduct of DCA.

Naming the pain in requirements engineering

Requirements Engineering (RE) has received much attention in research and practice due to its importance to software project success. Its interdisciplinary nature, the dependency to the customer, and its inherent uncertainty still render the discipline difficult to investigate. This results in a lack of empirical data. These are necessary, however, to demonstrate which practically relevant RE problems exist and to what extent they matter. Motivated by this situation, we initiated the Naming the Pain in Requirements Engineering (NaPiRE) initiative which constitutes a globally distributed, bi-yearly replicated family of surveys on the status quo and problems in practical RE. In this article, we report on the qualitative analysis of data obtained from 228 companies working in 10 countries in various domains and we reveal which contemporary problems practitioners encounter. To this end, we analyse 21 problems derived from the literature with respect to their relevance and criticality in dependency to their context, and we complement this picture with a cause-effect analysis showing the causes and effects surrounding the most critical problems. Our results give us a better understanding of which problems exist and how they manifest themselves in practical environments. Thus, we provide a first step to ground contributions to RE on empirical observations which, until now, were dominated by conventional wisdom only.

Cross- vs. within-company cost estimation studies revisited: an extended systematic review

[Objective] The objective of this paper is to extend a previously conducted systematic literature review (SLR) that investigated under what circumstances individual organizations would be able to rely on cross-company based estimation models. [Method] We applied the same methodology used in the SLR we are extending herein (covering the period 2006-2013) based on primary studies that compared predictions from cross-company models with predictions from within-company models constructed from analysis of project data. [Results] We identified 11 additional papers; however two of these did not present independent results and one had inconclusive findings. Two of the remaining eight papers presented both, trials where cross-company predictions were not significantly different from within-company predictions and others where they were significantly different. Four found that cross-company models gave prediction accuracy significantly different from within-company models (one of them in favor of cross-company models), while two found no significant difference. The main pattern when examining the study related factors was that studies where cross-company predictions were significantly different from within-company predictions employed larger within-company data sets. [Conclusions] Overall, half of the analyzed evidence indicated that cross-company estimation models are not significantly worse than within-company estimation models. Moreover, there is some evidence that sample size does not imply in higher estimation accuracy, and that samples for building estimation models should be carefully selected/filtered based on quality control and project similarity aspects. The results need to be combined with the findings from the SLR we are extending to allow further investigating this topic.

MPS.BR: A Tale of Software Process Improvement and Performance Results in the Brazilian Software Industry

In December 2003 a Brazilian nationwide program was created aiming at improving software processes. The MPS.BR Program is responsible for a great evolution in the software development scenario in Brazil by providing the means to support software process improvement initiatives based on the MPS Model. In 2008 a project named iMPS was launched to enable the analysis of the Performance Results of organizations that adopted the MPS Model. This paper describes some of the findings of the studies that have already been conducted. Preliminary evidences show improvement trends regarding cost, quality, schedule, and productivity, especially for those organizations that adopted the MPS Model for continuous improvement purposes.

Naming the Pain in Requirements Engineering: Comparing Practices in Brazil and Germany

As part of the Naming the Pain in Requirements Engineering (NaPiRE) initiative, researchers compared problems that companies in Brazil and Germany encountered during requirements engineering (RE). The key takeaway was that in RE, human interaction is necessary for eliciting and specifying high-quality requirements, regardless of country, project type, or company size.

Engineering Process Improvement in Heterogeneous Multi-disciplinary Environments with Defect Causal Analysis

Multi-disciplinary engineering environments, e.g., in automation systems engineering, typically involve different stakeholder groups and engineering disciplines using a variety of specific tools and data models. Defects in individual disciplines can have a major impact on product and process quality in terms of additional cost and effort for defect repair and can lead to project delays. Early defects detection and avoidance in future projects are key challenges for project and quality managers to improve the product and process quality. In this paper we present an adaptation of the defect causal analysis (DCA) approach, which has been found effective and efficient to improve product quality in software engineering contexts. Applying DCA in multi-disciplinary engineering environments enables a systematic analysis of defects and candidate root causes, and can help providing countermeasures for product and process quality. The feasibility study of the adapted DCA has shown that the adaptation is useful and enables improving defect detection and prevention in multi-disciplinary engineering projects and fosters engineering process improvement.

MPS.BR Program and MPS Model: Main Results, Benefits and Beneficiaries of Software Process Improvement in Brazil

Over the past years Brazil has done big investments in software quality. In 2010 the investment was among the highest in the world. Among them, the long term MPS. BR program focuses on improving the software process quality of Brazilian companies. Therefore, this paper intends to describe its software process improvement reference model (MR-MPS) and the most important results accomplished to date on Government, Industry and University scenarios. The Brazilian Government had endorsed the MPS adoption as a mean to increase the software industry competitiveness both locally and globally. In the University scenario, the MPS model contributes to transfer software engineering knowledge to the Industry and allows the identification of new research challenges. The quantification of benefits in the Industry is based on the iMPS family of surveys which allow both performance results characterization and performance variation analysis considering organizations that adopted the MPS model. Quantitative data have shown the MPS model acceptance by the Industry. Moreover, the obtained results evidence improvements reduction of cost and schedule and increase of productivity and quality in the software projects.

Using Forward Snowballing to update Systematic Reviews in Software Engineering

Background: A Systematic Literature Review (SLR) is a methodology used to aggregate relevant evidence related to one or more research questions. Whenever new evidence is published after the completion of a SLR, this SLR should be updated in order to preserve its value. However, updating SLRs involves significant effort. Objective: The goal of this paper is to investigate the application of forward snowballing to support the update of SLRs. Method: We compare outcomes of an update achieved using the forward snowballing versus a published update using the search-based approach, i.e., searching for studies in electronic databases using a search string. Results: Forward snowballing showed a higher precision and a slightly lower recall. It reduced in more than five times the number of primary studies to filter however missed one relevant study. Conclusions: Due to its high precision, we believe that the use of forward snowballing considerably reduces the effort in updating SLRs in Software Engineering; however the risk of missing relevant papers should not be underrated.

Preventing Incomplete/Hidden Requirements: Reflections on Survey Data from Austria and Brazil

[Context] Many software projects fail due to problems in requirements engineering (RE). [Goal] The goal of this paper is analyzing a specific and relevant RE problem in detail: incomplete/hidden requirements. [Method] We replicated a global family of RE surveys with representatives of software organizations in Austria and Brazil. We used the data to (a) characterize the criticality of the selected RE problem, and to (b) analyze the reported main causes and mitigation actions. Based on the analysis, we discuss how to prevent the problem. [Results] The survey includes 14 different organizations in Austria and 74 in Brazil, including small, medium and large sized companies, conducting both, plan-driven and agile development processes. Respondents from both countries cited the incomplete/hidden requirements problem as one of the most critical RE problems. We identified and graphically represented the main causes and documented solution options to address these causes. Further, we compiled a list of reported mitigation actions. [Conclusions] From a practical point of view, this paper provides further insights into common causes of incomplete/hidden requirements and on how to prevent this problem.