We suggest that expert programmers have and use two types of programming knowledge: 1) programming plans, which are generic program fragments that represent stereotypic action sequences in programming, and 2) rules of programming discourse, which capture the conventions in programming and govern the composition of the plans into programs. We report here on two empirical studies that attempt to evaluate the above hypothesis. Results from these studies do in fact support our claim.

Empirical Studies of Programming Knowledge

A set of properties of syntactic software complexity measures is proposed to serve as a basis for the evaluation of such measures. Four known complexity measures are evaluated and compared using these criteria. This formalized evaluation clarifies the strengths and weaknesses of the examined complexity measures, which include the statement count, cyclomatic number, effort measure, and data flow complexity measures. None of these measures possesses all nine properties, and several are found to fail to possess particularly fundamental properties; this failure calls into question their usefulness in measuring synthetic complexity. >

Evaluating Software Complexity Measures

A sufficiency theory is presented of the process by which a computer programmer attempts to comprehend a program. The theory is intended to explain four sources of variation in behavior on this task: the kind of computation the program performs, the intrinsic properties of the program text, such as language and documentation, the reason for which the documentation is needed, and differences among the individuals performing the task. The starting point for the theory is an analysis of the structure of the knowledge required when a program is comprehended which views the knowledge as being organized into distinct domains which bridge between the original problem and the final program. The program comprehension process is one of reconstructing knowledge about these domains and the relationship among them. This reconstruction process is theorized to be a top-down, hypothesis driven one in which an initially vague and general hypothesis is refined and elaborated based on inf ormation extracted from the program text and other documentation.

Towards a theory of the comprehension of computer programs

The internal program representation chosen for a software development environment plays a critical role in the nature of that environment. A form should facilitate implementation and contribute to the responsiveness of the environment to the user. The program dependence graph (PDG) may be a suitable internal form. It allows programs to be sliced in linear time for debugging and for use by language-directed editors. The slices obtained are more accurate than those obtained with existing methods because I/O is accounted for correctly and irrelevant statements on multi-statement lines are not displayed. The PDG may be interpreted in a data driven fashion or may have highly optimized (including vectorized) code produced from it. It is amenable to incremental data flow analysis, improving response time to the user in an interactive environment and facilitating debugging through data flow anomaly detection. It may also offer a good basis for software complexity metrics, adding to the completeness of an environment based on it.

The program dependence graph in a software development environment

While prior research has found that familiarity is beneficial to team performance, it is not clear whether different kinds of familiarity are more or less beneficial when the work has different types of complexity. In this paper, we theorize how task and team familiarity interact with task and team coordination complexity to influence team performance. We posit that task familiarity is more beneficial with more complex tasks (i.e., tasks that are larger or with more complex structures) and that team familiarity is more beneficial when team coordination is more difficult (i.e., for larger or geographically dispersed teams). Finally, we propose that the effects of task familiarity and team familiarity on team performance are complementary. Based on a field study of geographically distributed software teams, two of our hypotheses are disconfirmed: Our results show that the beneficial effects of task familiarity decline when tasks are more structurally complex and are independent of task size. Conversely, the hypotheses for team familiarity are confirmed as the benefit of team familiarity for team performance is enhanced when team coordination is more challenging---i.e., when teams are larger or geographically dispersed. Finally, surprisingly, we find that task and team familiarity are more substitutive than complementary in their joint effects on team performance: Task familiarity improves team performance more strongly when team familiarity is weak and vice versa. Our study contributes by revealing how different types of familiarity can enhance team performance in a real-world setting where the task and its coordination can be highly complex.

/pdf/familiarity-complexity-and-team-performance-in-16086ievby.pdf

Familiarity, Complexity, and Team Performance in Geographically Distributed Software Development

Three software complexity measures (Halstead's E, McCabe's u(G), and the length as measured by number of statements) were compared to programmer performance on two software maintenance tasks. In an experiment on understanding, length and u(G) correlated with the percent of statements correctly recalled. In an experiment on modification, most significant correlations were obtained with metrics computed on modified rather than unmodified code. All three metrics correlated with both the accuracy of the modification and the time to completion. Relationships in both experiments occurred primarily in unstructured rather than structured code, and in code with no comments. The metrics were also most predictive of performance for less experienced programmers. Thus, these metrics appear to assess psychological complexity primarily where programming practices do not provide assistance in understanding the code.

Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics

This experiment is the third in a series investigating characteristics of software which are related to its psychological complexity. A major focus of this research has been to validate the use of software complexity metrics for predicting programmer performance. In this experiment we improved experimental procedures which produced only modest results in the previous two studies. The experimental task required 54 experienced Fortran programmers to locate a single bug in each of three programs. Performance was measured by the time to locate and successfully correct the bug. Much stronger results were obtained than in earlier studies. Halstead's E proved to be the best predictor of performance, followed by McCabe's v (G) and the number of lines of code.

Third time charm: Stronger prediction of programmer performance by software complexity metrics

Third-time charm: stronger prediction of programmer performance by software complexity metrics

For the past two years the Software Management Research Unit at General Electric has been investigating several areas of human factors in software engineering with support from Engineering Psychology Programs of the Office of Naval Research. There have been two major thrusts in this research. The first thrust investigated the effects of several modern programming practices on programmer efficiency. The second thrust investigated the prediction of programmer performance from software complexity metrics such as those proposed by Halstead and McCabe. This research program consisted of separate experiments on the understanding, modification, debugging, and construction of software, each using professional programmers. Each experiment investigated both the effects of experimentally manipulated programming practices, and the values of complexity metrics computed from the programs employed.

First-year results from a research program on human factors in software engineering

Third Time Charm: Stronger Replication of the Ability of Software Complexity Metrics to Predict Programmer Performance.

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