Are software engineers true engineers?

Software engineering is an often used term to describe the activities, methods, and tools of large scale software development. There is an ongoing discussion whether Software Engineering can be considered as an engineering discipline. In many respects the development of software shares common properties of other engineering disciplines. In contrast to “classical” engineering fields, theories, questions, and approaches from the social sciences are more important than those from the natural sciences. This is investigated here by comparing the situation found in software engineering with several concepts of engineering in general. Three viewpoints are used to guide this comparison: one is a social stereotype of engineers; the second is the organization of engineering profession organizations; and the third are capabilities which the engineers of the future will need. This revised version was published online in June 2006 with corrections to the Cover Date.     

Are we overlooking some usability testing methods? A comparison of lab,beta, and forum tests

Abstract

We compared the effectiveness of lab testing, beta testing, and forum testing at identifying software usability problems. Thirty participants were involved in the experiment, with ten participants in each of the three test conditions. The lab test involved participants performing prescribed scenarios with the software in a controlled lab environment, while human factors engineers recorded participant's problems. The beta test method had participants use the software in their own environment to perform their real world work and record their own problems. The forum test was similar to the beta test, except that the software was made available on a company-wide computer bulletin board and the participants selected themselves. Findings show that the beta test method was as effective as the lab test method in the number of problem types identified. The lab test uncovered a larger proportion of serious usability problems than did the beta test. The beta test method was the most cost-effective method. The forum test method found the fewest number of problem types and was the least cost-effective. Thus, the results of this study broaden the current literature by showing that the beta test method may be a cost-effective alternative to the traditional lab test.     

What is software testing? And why is it so hard?

The author sheds some light on why testing today's software products is so challenging, and he identifies several solid approaches that all testers should be able to thoughtfully apply. The effective tester has a rich toolkit of fundamental testing techniques, understands how the product will be used in its operating environment, and has a nose for where subtle bugs might lurk in the product and a bag of tricks for flushing them out. The methods described can help testers provide a sensible answer to the question of what they really mean when they say they have finished testing a software system     

Statistical significance testing––a panacea for software technology experiments?

Empirical software engineering has a long history of utilizing statistical significance testing, and in many ways, it has become the backbone of the topic. What is less obvious is how much consideration has been given to its adoption. Statistical significance testing was initially designed for testing hypotheses in a very different area, and hence the question must be asked: does it transfer into empirical software engineering research? This paper attempts to address this question. The paper finds that this transference is far from straightforward, resulting in several problems in its deployment within the area. Principally problems exist in: formulating hypotheses, the calculation of the probability values and its associated cut-off value, and the construction of the sample and its distribution. Hence, the paper concludes that the topic should explore other avenues of analysis, in an attempt to establish which analysis approaches are preferable under which conditions, when conducting empirical software engineering studies.     

Who Are You? The Trade-Off between Information Utility and Privacy

There's no doubt that personal information can be valuable to companies. People are willing to give up information about themselves not because they're stupid or because they're being tricked by evil corporations, but because it can sometimes be in their best interests to do so. From this perspective, the important questions are how users can provide or limit access to that information, what benefits they might receive in exchange for a bit of information, and how they perceive the value of those benefits.     

Should software testers use mutation analysis to augment a test set?

Mutation testing has historically been used to assess the fault-finding effectiveness of a test suite or other verification technique. Mutation analysis, rather, entails augmenting a test suite to detect all killable mutants. Concerns about the time efficiency of mutation analysis may prohibit its widespread, practical use. The goal of our research is to assess the effectiveness of the mutation analysis process when used by software testers to augment a test suite to obtain higher statement coverage scores. We conducted two empirical studies and have shown that mutation analysis can be used by software testers to effectively produce new test cases and to improve statement coverage scores in a feasible amount of time. Additionally, we find that our user study participants view mutation analysis as an effective but relatively expensive technique for writing new test cases. Finally, we have shown that the choice of mutation tool and operator set can play an important role in determining how efficient mutation analysis is for producing new test cases.     

Industry–academia collaborations in software testing: experience and success stories from Canada and Turkey

Collaboration between industry and academia supports improvement and innovation in industry and helps to ensure industrial relevance in academic research. However, many researchers and practitioners believe that the level of joint industry–academia collaborations (IAC) in software engineering (SE) is still relatively very low, compared to the amount of activity in each of the two communities. The goal of the empirical study reported in this paper is to characterize a set of collaborative industry–academia R&D projects in the area of software testing conducted by the authors (based in Canada and Turkey) with respect to a set of challenges, patterns and anti-patterns identified by a recent Systematic Literature Review study, with the aim of contributing to the body of evidence in the area of IAC, for the benefit of SE researchers and practitioners in conducting successful IAC projects in software testing and in software engineering in general. To address the above goal, a pool of ten IAC projects (six completed, two failed and two ongoing) all in the area of software testing, which the authors have led or have had active roles in, were selected as objects of study and were analyzed (both quantitatively and qualitatively) with respect to the set of selected challenges, patterns and anti-patterns. As outputs, the study presents a set of empirical findings and evidence-based recommendations, e.g.: it has been observed that even if an IAC project may seem perfect from many aspects, one single major challenge (e.g., disagreement in confidentiality agreements) can lead to its failure. Thus, we recommend that both parties (academics and practitioners) consider all the challenges early on and proactively work together to eliminate the risk of challenges in IAC projects. We furthermore report correlation and interrelationship of challenges, patterns and anti-patterns with project success measures. This study hopes to encourage and benefit other SE researchers and practitioners in conducting successful IAC projects in software testing and in software engineering in general in the future.     

Are team personality and climate related to satisfaction and software quality? Aggregating results from a twice replicated experiment

ContextResearch into software engineering teams focuses on human and social team factors. Social psychology deals with the study of team formation and has found that personality factors and group processes such as team climate are related to team effectiveness. However, there are only a handful of empirical studies dealing with personality and team climate and their relationship to software development team effectiveness.ObjectiveWe present aggregate results of a twice replicated quasi-experiment that evaluates the relationships between personality, team climate, product quality and satisfaction in software development teams.MethodOur experimental study measures the personalities of team members based on the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and team climate factors (participative safety, support for innovation, team vision and task orientation) preferences and perceptions. We aggregate the results of the three studies through a meta-analysis of correlations. The study was conducted with students.ResultsThe aggregation of results from the baseline experiment and two replications corroborates the following findings. There is a positive relationship between all four climate factors and satisfaction in software development teams. Teams whose members score highest for the agreeableness personality factor have the highest satisfaction levels. The results unveil a significant positive correlation between the extraversion personality factor and software product quality. High participative safety and task orientation climate perceptions are significantly related to quality.ConclusionsFirst, more efficient software development teams can be formed heeding personality factors like agreeableness and extraversion. Second, the team climate generated in software development teams should be monitored for team member satisfaction. Finally, aspects like people feeling safe giving their opinions or encouraging team members to work hard at their job can have an impact on software quality. Software project managers can take advantage of these factors to promote developer satisfaction and improve the resulting product.     

What can software engineers learn from manufacturing to improve software process and product?

The purpose of this paper is to provide the software engineer with tools from the field of manufacturing as an aid to improving software process and product quality. Process involves classical manufacturing methods, such as statistical quality control applied to product testing, which is designed to monitor and correct the process when the process yields product quality that fails to meet specifications. Product quality is measured by metrics, such as failure count occurring on software during testing. When the process and product quality are out of control, we show what remedial action to take to bring both the process and product under control. NASA Space Shuttle failure data are used to illustrate the process methods.     

Towards understanding the relationship between team climate and software quality—a quasi-experimental study

This paper describes an empirical study that examined the work climate within software development teams. The question was whether the team climate in software developer teams has any relation to software product quality. We define team climate as the shared perceptions of the team’s work procedures and practices. The team climate factors examined were West and Anderson’s participative safety, support for innovation, team vision and task orientation. These four factors were measured before the project using the Team Selection Inventory (TSI) test to establish subject climate preferences, as well as during and after the project using the Team Climate Inventory (TCI) test, which establishes the subject’s perceptions of the climate. In this quasi-experimental study, data were collected from a sample of 35 three-member developer teams in an academic setting. These teams were formed at random and their members were blind to the quasi-experimental conditions and hypotheses. All teams used an adaptation of extreme programming (XP) to the students’ environment to develop the same software system. We found that high team vision preferences and high participative safety perceptions of the team were significantly related to better software. Additionally, the results show that there is a positive relationship between the categorization of better than preferred, as preferred and worse than preferred climate and software quality for two of the teamwork climate factors: participative safety and team vision. So it seems important to track team climate in an organization and team as one (of many) indicators of the quality of the software to be delivered.

Open source software licenses: Strong-copyleft, non-copyleft, or somewhere in between?

Studies on open source software (OSS) have shown that the license under which an OSS is released has an impact on the success or failure of the software. In this paper, we model the relationship between an OSS developer's utility, the effort that goes into developing an OSS, his attitude towards the freedom to choose an OSS license, and the choice of OSS license. We find that the larger the effort to develop OSS, the more is the likelihood that the OSS license would be free from restrictions. Interestingly, the result holds even when all OSS developers prefer restrictive licenses or less-restrictive license. The results suggest that least-restrictive or non-copyleft license will dominate other types of OSS license when a large effort is required to develop derivative software. On the other hand, most-restrictive or strong-copyleft licenses will be the dominant license when minimal effort is required to develop the original OSS and the derivative software.     

Are there any relationships between technology and cultural values? A country-level trend study of the association between information communication technology and cultural values

Technology has significantly changed the way humans live. Information and communication technologies have advanced and given their important spillovers to other economic sectors. Sociocultural view of globalization posits that cultural values across the world are continuously changing. From the theoretical lens of technological determinism theory, technology of a society determines the development of its social structure and cultural values. Drawing on the sociocultural aspect of globalization and technological determinism theory, this study investigates the relationship between technology and national cultural values. Using secondary national-level data such as United Nations’ E-Government Survey and Global Innovation Index, we validate the proposed relationships. The results of the study indicate that technology is an important driver of cultural convergence pushing national cultures across the globe to converge toward two cultural values suggested by Hofstede: higher individualism and lower power distance. This study also finds that technology increases national confidence in financial markets through creation of increased transparency. The study has implications for policymakers at both national and global levels regarding how technology affects the evolution of human societies. Additional implications exist for the promotion of democracy.     

Should robots have nuclear arms? AI technology and SDI software

This paper analyzes the use of Artificial Intelligence (AI) in designing SDI software. Two major uses are foreseen: (1) as an aid to writing the ten million plus lines of trustworthy SDI software that will be needed; (2) as a component of the battle management software. This second use allows the software to be adequately responsive to SDI counter-measures and autonomous in a manner fitting overall SDI requirements. This paper suggests that these uses of AI in SDI are infeasible and that rather than creating greater security, they would be more likely lead to a nuclear war.     

Forecasting daily total ozone concentration—a comparison between neurocomputing and statistical approaches

The present paper develops three predictive models for daily total ozone concentration over Arosa, Switzerland. The models are artificial neural network, multiple linear regression, and persistence forecast. Each model was judged for their predictive ability using analysis of variance, Pearson correlation study, and scatterplot analysis. Prediction errors were computed for each model. After painstaking analysis it was established that artificial neural network produces better forecasts than the statistical approaches like multiple linear regression and persistence forecast models.